The Biggest Problem With Flying Cars Is On The Ground

The startups and investors that have sent hopes soaring for “flying cars” could be in for a rough landing, in more ways than one.

Hundreds of companies, new ones and legacy aviation players alike, are working on such vehicles—also called air taxis or eVTOLs (short for electric vertical take off and landing). Five such startups have gone public in the past 12 months. They are trying to shape a near future in which taking a flying cab is an economically viable alternative to taking a terrestrial one.

The biggest stumbling block to that sci-fi vision, though, is rather down-to-earth: Flying-car companies haven’t figured out how to site, permit and construct enough places for their vehicles to land and take off to allow a workable business model for making and operating sky taxis.

The problem could have huge implications for the nascent flying-car industry, and for any hope that we will be commuting by air anytime soon. Early entrants to the industry, such as Joby Aviation, Lilium, Wisk, Airbus and Archer Aviation, have focused on the challenges of designing and building flying cars that work, and getting them certified as safe by the Federal Aviation Administration. Those challenges are considerable. Setting aside the cost of designing a prototype flying car in the first place, the process of submitting those designs to the FAA, testing them to verify they meet the agency’s specifications, and revising them can take years and is projected by analysts to cost up to $1 billion all by itself.

To date, most of the early investors in these companies have behaved as if solving those challenges is 90% of the work required to make flying cars commercially viable, says Todd Petersen, a consultant at Palo Alto, Calif.-based Lacuna Technologies, which creates software for cities to help them manage their transportation networks.

That ignores a range of other issues once the vehicles are ready to fly: where they will land and take off, how they will be integrated into existing air-traffic control systems, and whether the public will accept a large number of newfangled and comparatively large aircraft flying over their homes. Sorting out all that regulation and ground infrastructure is the “second 90%” of the problem of rolling out flying cars, says Mr. Petersen.

Mihir Rimjha is a senior aviation consultant at HMMH, a firm that helps governments and businesses with transportation planning. He has studied what are called “vertiports”—like heliports, but for eVTOLs, in depth, in work commissioned by NASA. He says that building out networks of rooftop vertiports in U.S. cities, on buildings and parking decks, will be critical for making flying taxi services and even privately owned flying cars a viable means of transportation. And, he adds, the companies haven’t been realistic about the hurdles involved.

For starters, there is the problem of how many suitable vertiport sites exist in America’s major cities. Here are just some of the factors that affect that equation: noise, the lack of airspace not already claimed by airports in cities like New York, and the necessity of retrofitting existing structures to be strong enough to accommodate flying vehicles and also provide them massive bursts of electricity for charging.

Setting those aside, every place that a vertical-take-off-and-landing vehicle is intended to land must be relatively free of surrounding structures—not just today, but indefinitely, says Mr. Petersen. This necessity is spelled out in the FAA’s rules on helicopter landing pads, which most in the industry believe will be the model for rules governing vertiports. That in turn means that getting FAA permission for a landing pad for a flying car requires figuring out all of the “glide paths” that such a vehicle can use when approaching a landing spot, should it suffer a mechanical failure.

Preserving such glide paths could mean, for example, that owners of property adjacent to vertiports might never be allowed to build anything higher than the vertiport—a particularly tricky and potentially controversial issue if cities are to play host to many such vertiports.

New York City’s history with helipads may prove instructive. Getting clearance from the FAA for a private landing pad is challenging, and residents generally oppose them—as they did with one granted to Amazon during its attempt to build an HQ in Long Island City. A crash on the roof of the Pan Am building in 1977 that killed five people has had a chilling effect on rooftop helipads in the city ever since.

Joby and Archer, which both went public last year, have said they aim to gain vertiport access by joining with Softbank-funded Reef Technologies, which manages parking lots and multistory parking decks across the U.S. Joby said last year that its collaboration would give it access to “an unparalleled range of rooftop locations across all key metropolitan areas in the U.S., as well as a mechanism to fund the acquisition and development of new skyport sites.”

A spokeswoman for Joby said that the company is “focused initially on existing aviation infrastructure and convertible assets,” such as the aforementioned parking garages.

Archer declined to comment on its vertiport plans.

Erick Corona, head of product development at Wisk, says his company believes it can sell more than enough of its autonomous vehicles to users of existing heliport and airport infrastructure to create a viable business. Eventually, vertiport developers like SkyPorts—a partner of Wisk—will be able to create vertiports where there is demand.

Further in the future, flying cars could get their own dedicated highways in the sky, he adds.

Jeremy Ford, head of property strategy at Reef, says that many U.S. cities already have heliports, but that it is still “very early innings” for his company and for flying-car companies in terms of figuring out how vertiports will actually be constructed, and where they will be placed.

Ricky Sandhu is at the forefront of trying to solve the vertiport problem, and he says that skeptical analyses of what’s next for the flying car industry are “absolutely right and completely on the money.”

He is founder and chief executive of Urban-Air Port, which recently opened what it says is the world’s first operational urban vertiport, called Air One, in a parking lot near a train station in Coventry, England. Mr. Sandhu is an architect who has led design teams on major infrastructure projects. In 2017, while consulting for Airbus on its flying-car efforts, he had a lightbulb moment: For this new mode of transportation to really take off, someone needed to work with landlords, local air-traffic controllers, national governments and city zoning boards to give the vehicles places to touch ground.

Air One has for the past few weeks hosted around 10 drone flights a day. Despite close collaboration with local and national air-traffic controllers and the city of Coventry, which is funding the vertiport, Air One has had teething issues, says Mr. Sandhu. Recently, for example, a large cargo drone was supposed to fly from Air One and land on the roof of a parking deck elsewhere in the city. But the builders of an office tower under development near the flight path raised safety objections, so the drone could only take off, fly in a circle and land again.

“Without the proper infrastructure, investment in eVTOLs is at risk,” says Mr. Sandhu.

Infrastructure challenges have led Air, an Israeli startup, to pursue a different strategy: creating flying cars that will be owned by individuals and can hop between privately owned vertiports. Air intends to bypass the high level of FAA certification required of aircraft that carry passengers, and the need to operate vertiports, by selling its flying cars directly to individuals who will pilot them themselves, says CEO Rani Plaut. Some of the company’s customers are already planning vertiports attached to their homes, he adds.

Stock-market investors are showing skepticism about flying-car companies. In step with the general selloff of shares in tech companies that have yet to show a profit, the valuations of the five flying-car companies that have gone public in the past year via SPAC (Vertical, Joby, Archer, Lilium and Eve) have declined significantly since their peaks at the beginning of April. Joby alone has lost about $2.4 billion in value, or 35% of its value at its peak on March 31.

Skeptics point to other reasons for caution. Dr. Rimjha co-wrote a report published last year which found that almost none of the assumptions touted by air-taxi companies that have recently gone public seem realistic: not their cost-per-vehicle figures, or their assumptions about cost per mile to operate these vehicles, or the time it will take to turn these vehicles into a commercial service.

When it went public, Joby projected it would cost $1.3 million to build each vehicle. Antonio Trani, a professor of engineering at Virginia Tech and Dr. Rimjha’s co-author, estimates, based on decades of evaluating aircraft, that after the FAA is done certifying Joby’s vehicle, the true price will be between $2 million and $3 million. Joby has also predicted that operating costs for its vehicles will be 86 cents a passenger mile. Dr. Trani thinks the actual figure will be between $3 and $4 a passenger mile.

The analysis also found that companies won’t be able to put vertiports where people most want to travel. Taking into consideration all that’s required in the places in America’s cities where there could be the greatest demand for flying cars, like dense urban cores, “we can’t really find much space for vertiports, even on rooftops,” says Dr. Rimjha.

For future vertiports, “permitting is a real issue,” says Mike Whitaker, a former FAA administrator who is now chief commercial officer at Supernal, a subsidiary of Hyundai that is working on a flying car of its own. It’s possible that cities will be forced to put vertiports in more outlying and low-lying areas—abandoned shopping malls could be ideal—and that access to such an amenity could cause more real-estate development around such an asset, he adds.

That would add time to any such trip for commuters or even just rich people who want to get out of town—and based on historical evidence, that would have a big impact on how much they use such services, says Dr. Rimjha.

That could force air-taxi companies to rethink their business models, perhaps to focus on smaller markets, such as replacing part of the world’s existing fleet of helicopters.

In the short term, these forces mean that “regional air mobility”—flights between cities and towns—is a more likely application for eVTOLs than flights within cities, says Robin Riedel, co-leader of the McKinsey Center for Future Mobility and a partner at the consulting firm.

All the time and effort required to create the ground infrastructure for flying cars could also mean companies that can afford to play a long game could be the ones that ultimately succeed. If falling stock prices and a scarcity of investment force consolidation in the flying-car industry, this could mean legacy aerospace companies, not disrupters, might someday build our Jetsons future.

Read the news, and it won’t take long to find a story about the latest feat of artificial intelligence. AI passed the bar exam! It can help diagnose cancer! It “painted” a portrait that sold at Sotheby’s for $1 million!

My own great hope for AI: that it might simplify the everyday problem of meal planning.

Seem a bit unambitious? Think again. For more than two decades as a food writer, I’ve watched families struggle to get weeknight meals on the table. One big obstacle is putting in the upfront time to devise a variety of easy meals that fit both budget and lifestyle.

Meal planning poses surprisingly complex challenges. Stop for a minute and consider what you’re actually doing when you compile a weekly grocery list. Your brain is simultaneously calculating how many people are eating, the types of foods they enjoy, ingredient preferences (and intolerances), your budget, the time available to cook and so on. No wonder so many weeknights end with mediocre takeout.

Countless approaches have tried to “disrupt” the meal-plan slog: books, magazines, apps, the once-vaunted meal kits, which even delivered the ingredients right to your door. But none could offer truly personalized plans. Could AI succeed where others failed?

The Weird Old Days

I conducted my first tests of AI in the summer of 2023, with mixed results. Early versions of Open AI’s ChatGPT produced some usable recipes. (I still occasionally make its gingery pork in lettuce wraps.) But the shopping lists it created were sometimes missing an ingredient or two. Bots! They’re just like us!

Eager to please, the chatbot also made some comical culinary suggestions. After I mentioned I had a blender, it determinedly steered me to use the blender…for everything, including fried rice, which it recommended I whiz into a kind of gruel. While it provided a competent recipe for pasta with zucchini, thyme and lemon, it thought it would be brilliant to add marshmallows, which I’d mentioned I had in my pantry, to the sauce. As a friend said: “If you’re having AI plan the recipes for you, it should definitely be doing something better than what your stoned friend would make you at two in the morning.”

Early AI could plan meals for the week, but required a lot of hand-holding. Like an overconfident intern.

More Fully Baked

Eighteen months after those first attempts—about 1,000 years in AI time—I was ready to try again. In January, DeepSeek AI, a Chinese chatbot, grabbed headlines around the world for its capabilities and speed (and potential security risks). There were also new and improved versions of the chatbots I’d found wanting.

This time, I decided to experiment with ChatGPT, Anthropic’s Claude and DeepSeek. (To see how they compared to one another, see “Bytes to Bites,” below.)

From my first AI rodeo, I knew to use short, direct sentences and get very specific about what I wanted. “Think like an experienced family recipe developer,” I told DeepSeek. “Create a week’s worth of dinners for a family of four. At least three meals should be vegetarian. One person doesn’t like fresh tomatoes. We like Italian, Japanese and Mexican cuisine. All meals should be cooked within 60 minutes.”

For the next 24 seconds, the chatbot “reasoned” through my request, spelling out concerns as I watched, rapt: Would the person who doesn’t like fresh tomatoes eat marinara sauce? Black bean and sweet potato tacos are a nice vegetarian entree, but opt for salsa verde to avoid tomatoes. Lemony chicken piccata is fast, but serve with broccolini. It was…amazing. The consolidated shopping list the chatbot provided was error free.

I tried the same prompt with Claude and ChatGPT, with curiously similar results. With all the options in the world, both bots suggested black bean and sweet potato tacos, and chicken piccata. The recipes’ instructions varied, as did suggested side dishes.

Relationship Counselling

I decided to write a more detailed request. “Long prompts are good prompts,” said Dan Priest, chief AI officer for consulting firm PwC in the U.S. The more information you provide, the more the AI can “align with your expectations.” Don’t try to get everything right the first time, Priest said: “Have a conversation.”

Good advice. I admit, when I first began my tests, I was searching for weak spots. But I learned it’s crucial to refine requests. As Priest said, AI will consider your various demands and make trade-offs—though perhaps not the ones you’d make.

So I started talking to AI. I said I like to cook with seasonal ingredients—that my dream dinner is a night at Chez Panisse, the Berkeley restaurant where chef Alice Waters redefined rustic-French cooking as California cuisine. Within seconds I had gorgeous recipes for spring lamb chops with fresh herbs, and miso-glazed cod with spring onions and soba. When I asked to limit the budget to $200, the bot swapped in pork for pricey lamb and haddock for cod. I requested meals that adhered to guidelines from the American Heart Association, and recipes that used only what was in my fridge. No problem.

But would the recipes work? Chatbots don’t have experience cooking; they are Large Language Models trained to predict what word should follow the last. As any cook knows, a recipe that reads well can still end in disaster. To my surprise, the recipes I tested worked exactly as written by the chatbots—and took no longer than advertised. Even my luddite husband called Claude’s rigatoni with butternut squash, kale and brown butter “a keeper.”

As yet no chatbot can compete with Alice Waters—or my husband, for that matter—in the kitchen. (For more on that, see “How Do Real Cooks Rate AI?” below.) But I’ll keep asking AI to, say, create shopping lists for recipes I upload, or come up with a recipe for what I happen to have in the refrigerator—as long as I’m there to whisper in the chatbot’s ear.

Bytes to Bites

Which chatbot is right for your kitchen?

Any of the three chatbots we tested can deliver a working meal plan—if you know how to talk to it. My personal pick was Anthropic’s Claude, for its intelligent tone and creativity, followed by DeepSeek AI for its “reasoning.” AI “agents” such as Open AI’s Operator, can, in theory, order the food needed to cook your recipes, but the consensus is they need a bit more time to develop.

Open AI’s ChatGPT • I had quibbles with ChatGPT’s first round of recipes. The seasoning skewed bland—only one tablespoon of soy sauce for a large veggie stir fry. It had me start by sautéing my chicken piccata, which then got cold while the pasta cooked. ChatGPT was also annoyingly chipper in its interactions. Still, with a few requested revisions, its lemon and pea risotto was perfection.

DeepSeek AI • I was impressed with this chatbot’s “reasoning” and the way it balanced sometimes-conflicting requests. Its recipes were seasonal (without prompting) and easy to follow; its shopping list, error free. Its one unforgivable mistake: presuming a paltry number of stuffed pasta shells would feed my hungry family. Some have voiced security concerns over using a Chinese chatbot; I felt comfortable sharing my meal preferences with it.

Anthropic’s Claude • I felt like Claude “got” me. This encouraged me to chat with it, resulting in recipes I liked and that worked, like a Mexican pozole for winter nights. This bot does need prompting; its initial instructions for brown butter and crispy sage leaves would have flummoxed an inexperienced cook. But when I suggested it offer step-by-step instructions, it praised me, which made me think it was even smarter.

Try This at Home

Have a conversation. Even a very specific meal-planning prompt requires AI to make assumptions and choices you might oppose. Ask it to revise. Add additional requirements. Follow up for more specific instructions. Time spent up front will deliver a more successful plan.

Role-play. Ask AI to think like a cook whose food you enjoy. (Told I like writer Tamar Adler’s recipes, Claude instantly offered one for wild mushroom bread pudding.) If you aren’t a skilled cook, it’s probably unwise to ask AI to mimic a three-star chef. Instead, ask it to simplify recipes inspired by your idol.

Read carefully and use common sense. It is always important to read through a recipe before you shop or set up in the kitchen, and this is especially true with AI. Recipes are invented on the fly and not tested. Ask for clarification if necessary, or a rewrite based on your skills, equipment or time.

Ask for a consolidated shopping list. In seconds, AI can aggregate the ingredients for your recipes into a single grocery list. Ask for total pounds or number of packages needed. (This saves you having to figure out, for example, how many red peppers to buy for 2 cups diced.)

Request cook times and visual cues. A good recipe writer lets you know how things will look or feel as they cook. Ask AI for the same. This will improve a vague “Bake for 20 minutes” to “bake for 20 minutes or until golden brown and the cake springs back to the touch.”

How Do Real Cooks Rate AI?

We asked AI to create dishes in the style of three favourite cooks, which it does base on text from the Internet and elsewhere it’s been trained on. And then we asked the cooks to judge the results. Verdict: The recipes didn’t reflect our panel’s expertise or attention to detail. Seems AI can’t replace them—yet.

Tamar Adler undefined Trained to cook at seminal restaurants including Prune and Chez Panisse; food writer, cookbook author, podcaster

AI dishes inspired by Tamar: Winter Squash and Wild Mushroom Bread Pudding; Braised Lamb Shoulder With White Beans and Winter Herbs; Pan-Roasted Cod With Leeks and Potatoes

Assessment: “Superficially, the recipes seem great and like recipes I would write.”

Critiques: “So much of everything I’ve written has been geared toward helping cooks build community and capability. Here, a cook is neither digging in and learning by trying and failing and repeating and growing; nor are they talking to another person, exchanging advice, smiles, jokes, ideas, updates.”

GRADE: C

Nik Sharma undefined Molecular biologist turned chef; editor in residence, America’s Test Kitchen; cookbook author

AI dishes inspired by Nik: Black Pepper and Lime Dal With Crispy Shallots; Roasted Spring Chicken With Black Cardamom and Orange; Roasted Winter Squash and Root Vegetables With Maple-Miso Glaze

Assessment: “A bit creepy. It’s trying too hard to imitate me but leaving out my intuition and propensity to experiment.”

Critiques: “Ingredients are not listed in order of use, and quantities and cook times are off. Black cardamom would kill that chicken. Also: I always list volumes for liquids and weights, whenever possible.” (AI did not—but you could ask it to!)

GRADE: C

Andrea Nguyen undefined Leading expert on the cuisine of Vietnam, cookbook author, cooking teacher, creator of Viet World Kitchen

AI dishes inspired by Andrea: Quick Lemongrass Chicken Bowl; Winter Vegetable Banh Mi With Spicy Mayo; Quick-Braised Ginger Pork with Winter Citrus

Assessment: “Machine learning is good for certain things, like getting factual questions answered. AI mined my content near and far, and got some things right but not others. Good recipes contain nuances in instructions that offer visual and taste cues.”

Critiques: “Quantities were off—often way off. The rice bowl is only good for a desperate moment. The ginger pork is an awful mash up of ideas. Yuck.”

GRADE: C/C+