AI vs Chef Who Designs the Better Seafood Dish When the Stakes Are Real?

Last spring, in a small Brooklyn kitchen, a line cook removed a pan of miso glazed black cod from the burner and placed it next to an almost similar fillet, one created by a human chef and the other by an AI chatbot that had never smelled fish before. Under fluorescent light, the two plates appeared to be nearly identical.

Vaguely. The chef’s version featured a somewhat uneven glaze that was darker on one edge where the flame had touched it for an extended period of time. When a home cook followed the AI’s recipe exactly, it was geometrically flawless. And for some reason, there seemed to be a gap in that perfection.

This type of experiment has becoming strangely prevalent. For more than a year, Michelle Meng, the owner of the TikTok account @hashslingers, has been competing AI generated recipes with those from restaurants and expert chefs. For those seeking a clear narrative, her discoveries are both intriguing and a little inconvenient.

In her own terms, the outcome has been a toss up. When it comes to meals that call for a high level of technical expertise, like baking or anything involving yeast, chefs typically prevail, and they nearly always win when the cuisine has cultural significance. AI, on the other hand, does remarkably well versus quick food restaurants and sometimes manages to win by adding more complicated ingredients than a human would consider using. The one occasion when AI defeated a well known chef? Scrambled eggs by Gordon Ramsay. No one anticipated that.

However, seafood is a different story. Fish, shellfish, and everything that is extracted from the ocean may be the hardest category for any algorithm to learn. The explanation is straightforward but difficult to measure: seafood varies greatly. When exposed to heat, halibut that was fished off the coast of Alaska in March acts differently than that which was caught in September. The brininess of shrimp from the Gulf of Mexico differs from that of their farmed Asian cousins. With a spatula, a chef who has worked at a fish station for years may sense these variations. No AI can.

AI recipe generators seem to be truly helpful for what you would refer to as fridge cleanout cooking the Tuesday night rush when you have some cherry tomatoes, withering spinach, and leftover salmon that need to be eaten. You can receive a feasible meal idea in a matter of seconds by entering those elements into a chatbot. While acknowledging this usefulness,

the seafood education portal Dish on Fish warns that AI frequently ignores important details like interior temperatures, food safety for raw preparations, and the drastically different cooking techniques needed for, say, scallops versus sole. It’s not that AI consistently makes risky mistakes. When it does go wrong, it does so in ways that are more significant with seafood than with nearly any other protein.

However, the larger dilemma is one of soul rather than safety. AI works better as a collaborator rather than a replacement, according to Prashant Ojha, CEO of Merlot & Co., a culinary technology business. He cites personalization as one area where algorithms could actually improve the dining experience:

picture a restaurant that creates a seafood course based on your dislike of excessively rich preparations, your preference for citrus forward sauces, and your shellfish allergy. That isn’t precisely cooking. However, it’s something that’s close by and difficult to ignore.

However, you can see how much of seafood cooking is done by the hands when you watch an experienced chef dissect an entire branzino. You’ll notice the confident cuts and the natural way to check for freshness by pushing your thumb against the flesh. Oysters, caviar, and seaweed are combined in Chef Dominique Crenn’s Story of the Sea course at Atelier Crenn in San Francisco to create a dish that reminds her of her early years spent close to the Brittany coast.

It wasn’t written by an algorithm. It originated from decades of living close to water, from memories, and from the salt air. The flavor chemicals in those substances may be examined by an AI to determine whether they work well together. But it couldn’t tell you why they matter.

When Grant Achatz let ChatGPT design an entire menu at his Chicago restaurant Next in 2024, the results pushed him in directions he hadn’t expected. But crucially, Achatz didn’t serve the AI’s output raw. He tasted, adjusted, rejected some ideas entirely, and refined others through his own experience. The technology offered a different perspective his words, not mine and that perspective had value. But the final plate still belonged to the chef.

It’s still unclear whether AI will ever develop anything resembling true culinary intuition. The machines are getting better at suggesting ingredient pairings, and the recipes they generate are increasingly sophisticated in their instructions. Meng has noticed this evolution herself, noting that the step by step directions from ChatGPT have grown more detailed over time.

But sophistication and soul aren’t the same thing. A perfectly balanced ceviche means nothing if it doesn’t remind someone of a coastal afternoon. A technically flawless bouillabaisse is just soup if there’s no story simmering underneath.

For now, the most honest answer to who designs the better seafood dish isn’t chef or machine. It’s chef with machine the human hand guided by data but not governed by it. The algorithm can suggest that yuzu and uni share molecular affinities. But only a person who has eaten both, standing at a fish market in Tokyo at six in the morning, knows whether that pairing belongs on the plate tonight. Data may map flavors. Hands still make the food worth eating

i) https://www.eater.com/23620766/chatgpt-ai-recipes-versus-chefs-tiktok-who-made-it-better
ii) https://www.dishonfish.com/using-ai-for-dinner-ideas-how-to-cook-seafood-safely-and-successfully