NASA’s Artemis Launch Gives Boeing Chance to Restore Its Space Credibility

Aerospace company has long worked on NASA missions, but latest rocket has faced cost overruns and delays in recent years

The National Aeronautics and Space Administration’s scheduled test launch Monday of a new mega-rocket will give Boeing Co. another chance to prove it can pull off big national projects following past missteps.

Boeing is the biggest contractor for the agency’s Space Launch System, a 38-storey-tall rocket that is supposed to launch the Orion spacecraft without crew toward the moon—and in 2025 blast U.S. astronauts back there as part of NASA’s Artemis missions to explore space.

“We’re providing both the brains and muscle,” Boeing says on its website, “to make the next generation of human spaceflight possible.”

Boeing has a long history developing NASA vehicles and handling missions for the agency. The company helped deliver astronauts to the moon in the 1960s, and worked on Space Shuttle operations before that program ended more than a decade ago. It also provides support for the International Space Station for NASA.

Boeing’s space business has struggled more recently, including technical and management problems with the SLS. Stumbles with its separate Starliner spacecraft repeatedly delayed a flight for NASA, and that ship has lagged behind a competing vehicle from Elon Musk’s SpaceX.

A successful SLS launch would help Boeing restore its reputation as it competes for government contracts and engineering talent with startups.

“The SLS is just another opportunity for us to show how well Boeing can do space,” said John Shannon, a Boeing vice president who oversees the SLS program for the company. “This vehicle can do something that no other vehicle can do, and we haven’t had a rocket like this in 50 years.”

Mr. Shannon added the company is confident that two of the big parts of the mission that Boeing engineers worked on—the main stage of the rocket used during liftoff, and a propulsion system designed to give Orion a big push in space toward lunar orbit—will function as planned.

The test launch of SLS and Orion without crew was supposed to happen four years ago, but Boeing and other contractors faced technical slip-ups and challenges the NASA inspector general has cited as among the sources of delays and cost overruns.

The belated test launch comes after problems Boeing has faced elsewhere in its commercial, military and space segments.

Three years ago, Boeing botched a test launch of its Starliner space capsule, sending it into the wrong orbit and failing to dock with the International Space Station. Subsequent technical problems delayed a do-over until a successful Starliner test launch earlier this year. The company has booked $767 million in charges related to that program over the past three years.

“We need Boeing to get this right,” said Scott Pace, a former NASA official who is director of the Space Policy Institute at George Washington University. “There’s a long history in recent years of Boeing’s technical problems, which they’re trying to fix—I sure hope they do, because it’s a national asset and it needs to work.”

Any major problems with this initial Space Launch System test launch could set back NASA’s planned Artemis missions to the moon. Two years from now, astronauts are scheduled to be on Orion as another SLS rocket launches it into space. And as soon as 2025, NASA wants SLS to propel astronauts to lunar orbit, where they would get on a SpaceX lander to travel to the lunar surface.

The missions could lay the groundwork for a possible future lunar base and an eventual operation to Mars, according to plans NASA has laid out under Artemis.

The overall project also involves aerospace companies including Northrop Grumman Corp. and Lockheed Martin Corp. Those contractors also have at times faced technical issues and delays flagged by the space agency’s inspector general. Lockheed Martin years ago dealt with challenges related to flight software and valves used for Orion, while Northrop Grumman, responsible for booster rockets on SLS, did so with insulation and avionics, according to reports from NASA’s inspector general.

Building and testing a new generation of exploration spacecraft that meet NASA’s stringent requirements has been challenging, with supply chains posing difficulties in recent years, said Mike Hawes, a vice president and program manager for Orion at Lockheed. Wendy Williams, vice president for propulsion systems at Northrop Grumman, said the company has incorporated lessons from building boosters for the first Artemis flight into the second, reducing timelines and costs.

The SLS program took shape amid political wrangling between the Obama White House and Congress in 2010. The project adapted technology from NASA’s now-ended Space Shuttle program to develop the world’s most powerful rocket capable of propelling humans and big spacecraft far into space. Some critics dubbed it the “rocket to nowhere” or the “Senate Launch System.”

Congress initially sought to launch SLS in 2016, but NASA early on saw the first mission happening in 2018. NASA Inspector General Paul Martin has estimated each of the first four Artemis missions will cost $4.1 billion, a figure he said is unsustainable.

Mr. Martin’s office had flagged Boeing miscalculations related to the scope of the project, welding problems and other troubles. “There was poor planning and poor execution,” he said in congressional testimony earlier this year.

Mr. Shannon, the Boeing manager for SLS, has said the company faced difficulties with the infrastructure at a Louisiana facility where NASA wanted the company to build the rocket. He said the company underestimated how long it would take to get its suppliers to provide needed parts.

“The aerospace supply chain for human spaceflight had really atrophied,” he said, citing the end of NASA’s Space Shuttle program years earlier for that. “We had to go in and really reinvigorate that supply chain.”

As of a year ago, Boeing and one of its joint ventures were awarded contracts worth about $12 billion over more than a dozen years for SLS work, according to a NASA inspector general report from November. Those deals represented 59% of the total contract value for the rocket program. Unlike with other government contracts, Boeing hasn’t booked any charges for SLS because many of its agreements with NASA are so-called cost-plus contracts, meaning taxpayers foot the bill for cost increases.

Mr. Shannon said the SLS program is profitable for Boeing but added: “We feel like we have a responsibility to provide good value to the taxpayer.”

As part of an attempt to reduce future SLS costs, NASA is planning to restructure the program’s finances. While the space agency offered few details, a NASA spokeswoman said the plan involved “creating a more affordable and sustainable exploration framework” in the future by “shifting more responsibility to industry.”

Boeing Chief Executive David Calhoun said recently he didn’t want to expose the company to significant financial risk with SLS. He told the trade publication Aviation Week: “I want to prove it all out to be ready, but I’m not going to do silly things, like lose money for 10 years.”

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+