The Dock to Dish model has expanded across North and Central America. Above, artisanal fishermen on the Pacific coast of Costa Rica head out to sea seeking schools of abundant forage fish to supply the program.
In 2012, entrepreneur Sean Barrett founded Dock to Dish in Montauk, New York. It connected local fishermen and women with local chefs, enabling the chefs to serve hyper-fresh seafood – with the caveat that they didn't know what would be on their menus until it arrived in their kitchens the night before.
"Since we're not a seafood-centric culture, people don't know what's what, where fish are from, and when they're in season, making them easy to dupe."
In June of 2017, The United Nations Foundation designated Dock to Dish as one of the top breakthrough innovations that can scale to solve the ocean's grand challenges. His company has since expanded across the Americas and has just opened up shop in Fiji. Leapsmag recently chatted with Barrett about his inspirations and ideas for how to overcome the hurdles of farming wild seafood. This interview has been edited and condensed for clarity.
What inspired you to start Dock to Dish?
The short story is "A Tale of Two Hills."
The first is Quail Hill Farm in Amagansett. I grew up in the commercial fishing port of Chinicock in the 1980's and 90's, working on my family's dock from an early age and in the restaurant industry in my teens. By my thirties, I had accrued my 10,000 hours of experience in both dock and dish. I watched the food system shift from local to global, especially in seafood. By the early 2000's, over 90 percent of seafood in the U.S. was imported. It was bad.
Quail Hill was the first CSA [Community Supported Agriculture, in which customers pay up front for a share in whatever crops grow (or don't) on the farm that season] in the U.S., founded in 1990. So people in the area were accustomed to getting their produce that way. Scott Chaskey, the poet farmer at Quail Hill, really helped crystallize the philosophy for me and inspired me to apply it to seafood. Fishermen had always been bringing a share of their day's catch to their neighbors; now we were just doing it in a more formalized way.
The second is Blue Hill at Stone Barns. [Executive chef and co-owner] Dan Barber literally trademarked the phrase "Know Thy Farmer"; we just expanded it to Know Thy Fisherman and it took off like a rocket ship. His connections in the restaurant world were also indispensable.
17th generation Montauk fisherman Captain Bruce Beckwith (above left) with crew Charlie Etzel (Center) and Jeremy Gould (right).
Do you have any issues that are unique to seafood that a CSA or meat co-op wouldn't face?
This food is WILD. People are totally disconnected from what that word means, and it makes seafood different from everything else. Everything changes when viewed through the prism of that word.
This is the last wild food we eat. It is unpredictable, and subject to variables ranging from currents and tides to which way the wind is blowing. But it is what makes our model so much more impactful and beneficial than the industrialized, demand-driven marketplace that surrounds us. The ocean and its ecosystem are the boss, not chefs and consumers.
There has a been a lot of press about seafood being mislabeled. How and why does that happen? Can Dock to Dish fix it?
Imported, farmed seafood is cheap. Wild, sustainable seafood is not. People are buying low and selling high to make a buck; and while fisheries are extraordinarily regulated, the marketplace isn't. There is no punishment for mislabeling, and no means to correct it. Since we're not a seafood-centric culture, people don't know what's what, where fish are from, and when they're in season, making them easy to dupe. But technology is poised to fix that; DNA testing can test what a fish sample is and where it's from, and SciO handheld spectrometers – soon to be incorporated into smartphones – can analyze the molecular makeup of anything on your plate.
We've created the first ever live tracking system and database for wild fisheries. It is similar to the electronic system used to monitor commercial fisheries, thanks to which the resurgence of wild seafood in U.S. waters is a model for the rest of the world. We have vessel tracking devices on our fishing boats and delivery vans, so the path of each fish is publicly available in real time.
In 2017, Dock to Dish launched the world's first live "end-to-end" tracking system for wild seafood, which provides full chain transparency and next-generation traceability for members.
People are increasingly looking to seafood as a healthier, possibly more sustainable protein option than meat. Can Dock to Dish scale up to accommodate this potentially growing market?
Nope. We can't scale; the supply is finite. That's why the price keeps going up. To avoid becoming "fish for the rich" we are working closely with Greenwave.org to create a network of 3D restorative ocean farms growing kelp and shellfish, which sequester carbon and nitrogen out of the air and soil. Restorative, because sustainable is no longer an option. In fifty years, a plate of seafood will be mostly ocean vegetables with a small amount of finfish as a garnish.
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
