The Gurus Who Say They Can Make Quiet Quitting Disappear

His name is Dean Lindsay, though that’s not what he goes by on LinkedIn. “Quiet Quitting Keynote Speaker” is this search-savvy consultant’s new moniker, and he says it’s helping him get hired—at $10,000 to $15,000 a day—by companies sweating the latest buzzy term for employee disengagement.

Mr. Lindsay, who has been advising businesses about corporate culture for two decades, says quiet quitting is closely related to burnout, work-life balance, stress management and other phenomena that came before. His prescriptions are largely the same, too.

When he saw the viral TikTok phrase had quickly migrated from social media to the C-suite, compelling many bosses to think about how to stop workers from checking out, he didn’t hesitate to rebrand, swapping out his name on LinkedIn for something catchy and of-the-moment.

“I just jumped on it,” he says.

If you’re running a company now, chances are your inbox is full of messages from experts claiming they can goose morale, foster connection, boost buy-in and make various other jargon-studded dreams come true. The people who claim to know the most about quiet quitting are real go-getters, it turns out.

The extent of the problem these consultants aim to solve, and whether it’s new, is debatable. Many of them say that’s beside the point. Getting people to care more deeply about their jobs and colleagues may be a perpetual corporate mission, but it’s an important one, the argument goes. So what if it took a meme to intensify the sense of urgency?

Some, like Mr. Lindsay, run rousing workshops full of motivational mnemonics. (It’s all about the six P’s of progress, he says: pleasure, peace of mind, profit, prestige, pain avoidance and power.)

Less experienced consultants advertise youth as an advantage, saying they can get through to millennials and Gen Z.

Still others offer to set up employee-driven charitable campaigns, using company dollars, to make people feel better about where they work.

Rising Team, a Palo Alto, Calif., startup that sells camaraderie-building software designed to reduce quitting (quiet or otherwise), just closed a second venture-capital round, bringing total investments to $6 million.

For human-resources leaders, the pitches can seem endless.

Priti Patel, chief people officer at G2, a technology marketplace, says she gets daily emails about solving burnout and quiet quitting.

“I don’t even count anymore,” she says.

While some solicitations strike her as gimmicky, Ms. Patel says she doesn’t roll her eyes at all of them. She landed her current position last year after first working with the company as an independent “conscious leadership” coach, which she describes as helping managers deepen their emotional intelligence.

Her take on quiet quitting is that it’s simply the notion of having boundaries at work— hardly new. Nevertheless, establishing the boundaries is a real challenge for managers and direct reports alike, she says, and sometimes an outsider can help set expectations that work for everyone.

Karyn Twaronite, Ernst & Young’s global diversity, equity and inclusion officer, adds that HR consultants can lend valuable perspectives if they represent the views of young people or others who are missing or rare in the executive ranks. EY uses a mix of internal and external advisers, she says, and conducts quarterly “pulse” surveys, asking whether employees feel that they belong at the firm—which last month started splitting its consulting and auditing businesses—and are free to be themselves.

“These feel like softer things, but we know that they’re critical because if people don’t feel this way, then they could, in theory, quit,” she says. “If a consultant can help leaders listen to their employees or decipher the data, that’s really important.”

Data is a main selling point for Rising Team, the venture-funded startup that Facebook, Google and Yahoo veteran Jennifer Dulski launched in 2020. (She says her business idea predates the pandemic, but “the timing turned out to be perfect.”) Her young company starts by polling a client’s staff to measure the likelihood they’ll stay, and says in a few months it can deliver a meaningful increase in the share who plan to stick around.

Ms. Dulski, who teaches management at Stanford Graduate School of Business, aims to get co-workers to know and like each other—and without resorting to hackneyed exercises like trust falls. Rising Team’s “kits,” as she calls the software, lead groups of employees through virtual or in-person discussions every six weeks or so. A kit for a 10-person team costs $99 a month, and companies with many teams can get discounts for buying in bulk.

The idea is that workers who are invested in their colleagues are less likely to slack off or leave.

Money helps, too, though raises and bonuses aren’t the only ways to promote loyalty and engagement, says Tess Murphy, director of strategic partnerships at Kiva, a microfinance nonprofit. Her pitch to companies is that they can pump up employee enthusiasm by letting every worker direct a small sum—as little as $50—to a favourite cause.

Kiva has managed these corporate programs for eight years, but the tumult of the past two has prompted more workers to consider whether they and their employers are making a difference in the world, Ms. Murphy says. Businesses, in turn, are grasping for initiatives that can give their people a sense of purpose.

Ms. Murphy says companies wonder, “ ‘How do we get them connected and excited about the work that we’re doing?’ ”

Much of their consternation centres on young workers who fixate on what is, or isn’t, in their job descriptions and put in too few hours for some of their older colleagues’ tastes, she says.

Appealing to executives who are confounded by their greenest employees, Adam Owens left a steady human-resources job and started his own consulting operation this year. He bills himself as an unconventional alternative to competitors with Ivy League M.B.A.s and decades of experience. If you’re a Boomer or Gen Xer trying to figure out Gen Zers, he says, hire someone like him, a former philosophy major who dropped out of college in the aughts and built a career without the typical credentials.

Many young workers aren’t unmotivated, he adds, but they don’t necessarily measure success like their predecessors or do what they’re supposed to do in the eyes of others. He aims to help bosses understand what these employees really care about.

“Millennials are uniquely positioned to deal with this challenge,” Mr. Owens says. “We function as a bridge between the other generations.”

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+

The Gurus Who Say They Can Make Quiet Quitting Disappear—for $15,000 a Day

Some have Ivy League degrees, others have no degrees, but these workplace consultants all say they’ve got an antidote to the viral trend of employee disengagement

With the debut of DeepSeek’s buzzy chatbot and updates to others, we tried applying the technology—and a little human common sense—to the most mind-melting aspect of home cooking: weekly meal planning.