Patrick Duffy of ‘Dallas’ Fame Lists Oregon Ranch for $14 Million

Former “Dallas” star Patrick Duffy is putting his roughly 390-acre Oregon ranch on the market for $14 million.

The property sits along the Rogue River outside the city of Medford in southern Oregon, according to Alan DeVries of Sotheby’s International Realty, who has the listing with colleague Matt Cook.

Mr. Duffy said he bought the first roughly 130 acres of the property in 1990 for roughly $1.5 million with his late wife, Carlyn Rosser. The couple spent roughly two decades and about $3 million buying surrounding properties when they went up for sale, said the actor, who has made the ranch his primary home since the early 2000s.

“My family always felt like we were stewards as opposed to owners,” said Mr. Duffy, 73. “We kept the boundaries sacred.”

Mr. Duffy said he first saw the property while fishing with a friend. The property contained a few structures, including what is now the main house, but was mostly wilderness, he said.

“It was pristine,” he said. “There was no paved road. There were some trails through the woods and about a mile—a little less than a mile—of river frontage.”

Mr. Duffy said he flew Ms. Rosser out to see the ranch, and they bought it. The main house has four bedrooms, and connects to a gallery where the couple displayed their art collection. They converted a caretaker’s cottage into a one-bedroom guesthouse with a loft. They also added a building that contains a hot tub overlooking the river, a structure for an indoor lap pool, and a wine cellar built into the side of a mountain, all within walking distance of each other.

As they purchased adjacent properties over the years, they acquired eight more houses and several pastures that are rented out to local ranchers. One of the homes was demolished, six are rented to tenants, and one is used as the ranch manager’s house, according to Mr. Duffy.

“We became a working ranch but not with our own animals,” he said. “It added the most beautiful, bucolic sense of the place.”

A homestead that dates back over 100 years still sits at the entrance to the property, he said. In it he found an old stove, which he restored and put in the main house. But the majority of the roughly 390 acres remains wilderness. The property now has approximately 2 miles of river frontage, according to Mr. DeVries.

For roughly a decade, Mr. Duffy and Ms. Rosser used the ranch as a family getaway from their primary home in Los Angeles. Then in the early 2000s, when their children went off to college, they decided to move there full time.

Ms. Rosser died in 2017, and Mr. Duffy said he plans to move full-time to either California or Colorado. He will keep a few parcels of land that aren’t attached to the main ranch, according to Mr. DeVries.

Mr. Duffy is well-known for his role as Bobby Ewing in the TV drama “Dallas,” which ran from 1978 to 1991. He also played Frank Lambert on the 1990s sitcom “Step By Step.” Today he runs an online sourdough business, called Duffy’s Dough, with his partner, Linda Purl.

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+