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The Spoon

Daily news and analysis about the food tech revolution

NotCo

Can AI Help Chocolate Survive? NotCo and Swiss Chocolate Maker Barry Callebaut Think So

Is the world of chocolate heading toward the same fate as the dodo bird?

It may be a surprise to some to consider that a centuries-old, worldwide favorite like chocolate is on its way out, but the reality is that most experts agree that climate change, ingredient shortages, rising prices, and other global pressures have put chocolate in jeopardy. Some even predict that it could one day become extinct.

The impending peril has meant that every global chocolate supplier has started looking for ways to adapt to the future, and increasingly, one of those ways is to do what many companies both in and outside of food are doing: look to AI to accelerate change. Some, like Hershey’s, have developed their own tools such as Atlas, while others are looking to companies with deep experience building AI models focused on food to help transform their business.

In that second category is a new partnership between Barry Callebaut, one of the world’s largest premium chocolate makers, and NotCo, the AI-powered food company that has made a name for itself in recent years with its Giuseppe food AI platform. The deal calls for NotCo AI to embed what it describes as its foundational AI platform directly into Barry Callebaut’s R&D pipeline. The announcement says the collaboration gives Barry Callebaut access to the same engine that has helped NotCo accelerate formulation cycles, solve complex ingredient challenges, and unlock unexpected flavor and functionality breakthroughs for global CPG brands.

For NotCo, the deal marks its most significant category-wide integration yet and reinforces what CEO Matias Muchnick said he and his cofounders believed from the very beginning. NotCo is not simply a maker of plant-based food. It is a next-generation R&D operating system for the food industry.

“This is exactly what we built NotCo for,” Muchnick said at SKS 2025 in July. “The value of our platform comes from a decade of high-fidelity data, from formulations and ingredient chemistry to sensory outputs and manufacturing parameters, all connected so we can solve multi-dimensional problems faster and with no human bias.”

The two companies plan to feed Barry Callebaut’s 100-year knowledge base and ingredient data into NotCo’s AI foundation model and build what they are calling the chocolate industry’s first end-to-end AI innovation hub. The goal is to iterate on new formulations, explore functional ingredients, and optimize for sustainability, cost, and Nutri-Score constraints.

At Future Food Tech in the spring, Muchnick gave a presentation that emphasized their push to become the go-to partner for AI transformation. The company’s Kraft Heinz partnership had already given them some street cred, so it is no surprise that they have seen strong interest from global food brands.

“Every big food company is having board-level conversations: do we have the technology to adapt to new consumers, shortages, and regulations? And consistently the answer is no,” Muchnick said at SKS in July. “That is why they’re coming to us. Everything changed in the last six months.”

In a sense, food AI specialists like NotCo are in a race against time as bigger general-purpose foundation models from OpenAI, Anthropic, and others become easier for different industries to customize with their proprietary data. Most CPGs do not yet have it in their DNA to build AI-forward development cycles, but that is likely to change in the next five years as boards demand transformation while they watch competitors accelerate product development and, in categories like chocolate, identify new alternatives in a market where environmental pressure, inflation, cost volatility, and other external factors force their hand.

“The companies that don’t adopt AI the right way will get the Blockbuster effect,” said Muchnick. “They’ll become obsolete. The future food companies will be AI companies.”

Are Big Food Companies Really Embracing AI?

While some companies like NotCo have positioned themselves as the OpenAI of the food world, the truth is that the AI transformation of the food industry is still in the first inning. That is partly because the food system itself, a mix of legacy CPG giants, agricultural suppliers, ingredient developers, and regulators, moves at a glacial pace.

In my recent conversation with Jasmin Hume, founder and CEO of Shiru, she confirmed that the industry is still in the early stages, in large part because food companies have massive amounts of data and strong confidence in their own research and development.

“Food companies have world-class R&D teams, and those scientists want to see proof before adopting a new tool. It’s a lot of tire-kicking in the first meetings,” said Hume.

This slow pace does not mean AI is not making inroads. It is simply happening beneath the surface. From discovery platforms like Shiru’s to optimization tools in manufacturing and retail analytics, AI is slowly reshaping how food gets made.

But the true question is not if the food system will use AI, but who will own the models that make it useful. The answer is usually tied to who owns the data. Legacy food and ingredient companies have decades, even centuries, of proprietary chemical, biological, and sensory data. This makes them both powerful and hesitant to engage with AI models that might use that data to build their own foundation models.

Big food brands “are not going to very quickly turn over that data,” said Hume. Many are debating whether to build their own in-house systems, using models fine-tuned on proprietary data that never leaves their servers. Others are beginning to explore partnerships with companies like NotCo and Shiru that specialize in the discovery layer.

That need for validation may be the biggest differentiator between food AI and other industries. As Hume put it, “You have to bring it into the lab and make sure that it actually works. Otherwise, the predictions are worthless.”

When Hume and I discussed whether large players like Microsoft or Google would eventually dominate vertical-specific foundation models, she acknowledged that possibility. However, she stressed that today’s large foundation models are not yet equipped to deal with the physical and regulatory realities of food. “There’s a ton of very specific know-how that goes into making those models usable for applications like protein discovery or formulation.”

For Shiru competitor NotCo, this highly specific data and domain knowledge are what the company is banking on to solidify its position as a key player in building a foundation model for food, a term now featured prominently on its website.

“I think what people need to understand is that AI is truly about the data sets and the value of the data sets that you have and the logic of the algorithms,” said Muchnick in an interview I had with him in July at Smart Kitchen Summit. “It’s really hard to get to where we were, and specifically also because we weren’t just an AI company. We are a CPG brand, and we learned a lot from being a CPG brand.”

In conversations with AI experts outside of food, several have said we are starting to see the big foundation models open up to allow companies to train them with vertical or highly specific domain knowledge. One pointed to Anthropic’s Model Context Protocol (MCP), which lets a foundation model connect to external data sets to process answers.

Another example is Thinking Machines’ newly announced fine-tuning API called Tinker, which could make it significantly easier for a food brand to train a model with domain-specific knowledge by removing the heavy infrastructure and engineering overhead typically required for custom AI development.

For Shiru, NotCo, and others developing food and ingredient-focused AI, there is still significant opportunity because the field is still so early.

“We’re just starting to see companies thinking about their own internal instances,” said Hume. “A lot of this is in progress, boardrooms are having these discussions right now.”

One of the biggest holdups for food brands is that data ownership and business-model alignment remain unsolved. Who owns the training data and the resulting outputs is a key question, and without clear answers, many companies will hold their data close, limiting the ability of shared platforms to reach critical mass.

For that reason, Hume believes partnerships and licensing models, not open data exchanges, will drive progress in the near term. Shiru’s model focuses on IP discovery and licensing, which allows the company to build intellectual property value without requiring massive manufacturing investments. “Our IP portfolio has doubled year over year since 2022,” said Hume. “Now the focus is on monetizing that through licensing and sales.”

The topic of food-specific foundation models and the adoption of AI by food brands is a fast-moving one, so you’ll want to make sure to listen to this episode to get caught up. You can listen to our entire conversation below or find it on Apple Podcasts, Spotify, or wherever you get your podcasts.

NotCo’s Next AI-Powered Innovation? Replicating Human Scent to Make My Dog Happy

As far as my family’s small Pomeranian, Zeus, is concerned, I’m a very distant second banana when it comes to the humans in his life. Sure, he’ll let me feed him and pay the cheese tax, but the reality is he’s only got eyes (and a nose) for one person in his life, which happens to be my wife.

Like many loyal dogs, when my wife is out of the house, Zeus finds comfort in lying on blankets, sweaters, or anything that may have a whiff of his favorite person’s scent. Where things get rough for the little guy is when we have to travel, but someday soon we’ll be able to bring a bottle of “mom” fragrance to provide a little canine aromatherapy when we drop him off at the dog sitter.

That’s at least according to NotCo CEO Matias Muchnick, with whom I sat down this week at the Smart Kitchen Summit to talk about what his company and the journey of being a pioneer in leveraging AI to develop new food (and now pet) products.

 “We’re partnering with one of the biggest pet companies in the world to generate human scent,” said Muchnick. “Literally, it’s like a 23andMe for your smell.” The idea according to Muchnick is to use an AI model to do scent profiling to create a mist that replicates your scent, helping ease separation anxiety for pets when their humans leave home.

NotCo Wants to Create the 23andMe of Scent for our Pets

There’s no doubt that this new direction is leveraging some of the work that NotCo has done in building out a “Generative Aroma Translator”, which the company unveiled at the Spoon’s Food AI Summit last fall. “The system intakes your prompt, such as ‘an ocean scent on a breezy summer day on a tropical island’ to create a novel chemical formulation of that scent in one-shot,” said the company’s former chief product officer, Aadit Patel.

Only add in an extra layer of personalization, which includes your odor and all the notes you pick up as you travel through the world.

“We will get you a report of your top notes of your own body whenever you get back home,” said Muchnick. “If you work in an office, it’s going to be an office, depending on the office that you work in. If you’re a mechanic, you’re going to have a lot of other odors.”

Muchnick kept quiet on who the partner is or what the actual product would look like, but did indicate this project is one of hundreds of new projects since the company doubled-down on being an AI-powered innovation engine for CPG brands.

“Our first investor decks in 2016 were all about AI,” Muchnick said. “But no one believed in it
back then, so we had to prove the model ourselves.” NotCo’s path to validation came by launching its own consumer products, such as mayo, ice cream, burgers, and capturing market share in Latin America and North America, after which big players couldn’t help but take notice.

Today, NotCo is firmly in phase two of its journey. Through partnerships with companies like Kraft Heinz, Starbucks, and PepsiCo, the company is showing how Giuseppe can help brands rapidly create new product formulations and adapt to regulatory or consumer-driven upheaval, such as the recent push to remove synthetic dyes or respond to GLP-1-driven shifts in eating habits. He said the company has over 50 active color replacement projects.

The different between now and just a couple years ago is drastic when it comes to big food’s receptivity to working with AI. Curiosity and hesitation has melted away and turned to eagerness and a sense of urgency.

Who he’s talking to has also changed. What used to be R&D director conversations are now CEO-level discussions. “AI is no longer optional,” said Muchnick. “If they don’t adapt, they’ll face the blockbuster effect. They’ll become obsolete.”

You can watch the replay of the full interview at The Spoon next week.

Matias Muchnick Reflects on Being Ten Years Early to the Food AI Party

Back in 2015, when Mattias Muchnick was contemplating the vision for his new company, he imagined building an AI-powered engine that would help big FMCG (fast-moving consumer goods) brands bring products to market faster.

While Muchnick was excited about AI’s potential to transform the food industry, few food executives at the time shared his enthusiasm. Back then, only large tech companies like IBM had experimented with artificial intelligence—such as using Watson’s mainframe-powered AI for food recipe development—but most food industry leaders didn’t view AI as an essential or even necessary tool for creating new products.

Fast forward to 2025, and you’d be hard-pressed to find a food executive who doesn’t recognize AI as strategically critical. This shift undoubtedly explains why Muchnick and his team attracted a packed room last week at Future Food Tech, filled with journalists, investors, startup founders, and major food brands eager to learn about the company’s early success and leadership in food AI.

During the session, Muchnick and his team highlighted their platform’s success (seven out of twenty top FMCG companies have already collaborated with NotCo), discussed the growing trend of food tech startups pivoting toward AI, and shared insights into future developments. Intrigued by the presentation, I decided to follow up with Muchnick afterward to explore these topics further.

In our subsequent interview, I asked Muchnick about the key lessons he’s learned over the past decade, his perspective—as a longtime pioneer in food AI—on the rise of ‘AI-washing,’ and what innovations he’s most excited about moving forward.

You can watch the full interview below:

The Spoon Interviews - Matias Muchnick



The Idea of Food ‘Teleportation’ Isn’t New—But AI Is Finally Making Distributed Digital Food Replication a Reality

Over the past decade, there’s been no shortage of attempts to better understand, map, and recreate food and its properties with the click of a mouse.

From new data ontologies to a proliferation of digital noses, we’ve seen incremental steps toward an envisioned world where the fundamental building blocks of food can be better understood. However, in the past year, there has been a rapid acceleration in our collective ability to digitize various properties of food, largely driven by advances in AI.

The latest example of this comes from Osmo, which recently announced its development of the ability to digitally “teleport” a scent by using AI to digitize and re-materialize it.

Scent Teleportation

As the company’s CEO, Alex Wiltschko, explained:

“We select a scent to teleport and introduce it to a machine called the GCMS (Gas Chromatography-Mass Spectrometry). If it’s a liquid, we inject it directly; if it’s a physical sample, like a plum, we use headspace analysis, trapping the scent in the air around the object and absorbing it through a tube. The GCMS identifies the raw data, which can be interpreted as molecules, and uploads it to a cloud. There, it becomes a coordinate on our Principal Odor Map — a novel, advanced AI-driven tool that can predict what a particular combination of molecules smells like. This formula is sent to one of our Formulation Robots, which treats it as a recipe and mixes different scents to replicate our sample.”

In other words, Osmo breaks down the building blocks (molecules), creates a map, and then sends this digital map to an essence “printer” that re-creates it.

This announcement comes just weeks after leaders of NotCo’s scent and flavor AI team shared research on their new generative AI that creates scent and flavor formulations. Here’s how Aadit Patel, NotCo’s head of product, described the model:

“The system takes your prompt—such as ‘an ocean scent on a breezy summer day on a tropical island’—and creates a novel chemical formulation of that scent in one shot.” The model then generates a corresponding fragrance formula. According to Patel, the model is built on a “natural language to chemical composition” framework, tokenizing molecules to create a system capable of understanding and generating novel combinations.

With years of work focused on digitally understanding, quantifying, mapping, and reproducing scents, flavors, and food building blocks, what is allowing these latest efforts to make such significant leaps?

In a word (or two): AI. In the past, creating a facsimile of a flavor or scent took thousands of hours, relying on trained experts to work in a lab or kitchen, drawing on years of expertise. Now, AI is expediting that process with orders of magnitude more efficiency, often leaving the expert to provide a final sign-off to ensure the AI-created formula meets standards for proximity to the desired result, as well as checks for safety, cost feasibility, and more.

For the record, Osmo isn’t the first company to discuss “teleporting” a formula for digital recreation. In 2018, Japanese startup Open Meals made headlines with its “sushi teleportation” demo, essentially sending 3D printing instructions to create a sushi-like meal. We also saw Cana’s ambitious attempt to make a Star Trek replicator (though, as it turns out, investors weren’t quite ready to enter the food teleportation age).

All of this follows years of efforts to quantify and understand food digitally, including the creation of ontologies for the Internet of Food and early attempts to use AI to analyze food. But over the past couple of years, there’s no doubt that parallel advances in AI (especially in large language models) and breakthroughs in food, olfactory, and chemical science are ushering in a world where true food “teleportation”—or, more accurately, the ability to understand and synthetically recreate food, flavors, and scents—has arrived.

I’m excited to see where this all goes. To manifest the vision laid out in science fiction over the years and imaginative product concepts like that of Open Meal required a true digital understanding of the molecular building blocks of food. With AI, we are closer than ever to that understanding, and the products we’ll see built in the coming decade will not only create some mind-blowing consumer experiences but also possibly fundamentally change how food and beverage products are made and distributed.

Live Event: Using Generative AI to Build Next-Generation Flavors & Fragrances

Join us today for a live event at 8:30 AM PT, featuring the project leads for NotCo’s new AI to develop flavors and fragrances. Register and watch below or head to Streamyard.

Can AI be used to create new flavors and fragrances?

As I wrote last week, food-tech company NotCo has been asking itself this question for the past couple of years. Their answer is a newly unveiled generative AI model, the Generative Aroma Transformer (GAT), that they say is capable of creating new flavor and fragrance formulations.

The company’s Senior vice president of Product, Aadit Patel, described how it works this way: “The system intakes your prompt, such as ‘an ocean scent on a breezy summer day on a tropical island’, to create a novel chemical formulation of that scent in one shot.” From there, the model generates a corresponding fragrance formula. According to Patel, the model is built on a “natural language to chemical composition” framework, tokenizing molecules to create a system capable of understanding and generating novel combinations.

NotCo says early tests have been extremely positive, and the company says their research indicates that GAT’s abilities rival those of human perfumers. At the Food AI Summit last month, the two product leads, Francisco Francisco Clavero and Cindy Sigler, gave an in-depth presentation on the science behind their new model and talked about early results.

Their presentation was fascinating, so I asked them to present to our Food AI Co-Lab community.

Watch the recorded session below:

Using Generative AI to Build Next-Generation Flavors & Fragances

NotCo Has Created A Generative AI for Flavor and Fragrance That Can Create Unique Formulations With Text Prompts

Food-tech company NotCo has developed a novel generative AI model, the Generative Aroma Transformer (GAT), capable of creating new flavor and fragrance formulations. The model, which the company presented on at the Food AI Summit last month, could be a potentially disruptive new tool that could impact a variety of consumer goods markets such as food, personal care, home care, and beauty industries.

The company’s Senior VP of Product, Aadit Patel, talked about the new model in a post on Linkedin today, highlighting how GAT can translate textual prompts into unique chemical formulations. “The system intakes your prompt, such as ‘an ocean scent on a breezy summer day on a tropical island’ to create a novel chemical formulation of that scent in one-shot.” From there, the model generates a corresponding fragrance formula. According to Patel, the model is built on a “natural language to chemical composition” framework, tokenizing molecules to create a system capable of understanding and generating novel combinations.

NotCo says early tests have been extremely positive. Their research indicates that GAT’s abilities rival those of human perfumers. In blind smell tests, fragrances created by GAT proved indistinguishable from those crafted by human experts. This finding signals a potentially significant shift in the industry, where only 600 certified perfumers exist globally.

How it Works

According to research presented at the Food AI Summit, the core of GAT’s functionality lies in its ability to understand and model complex interactions between volatile molecules. The model is trained on an extensive dataset of historical fragrance formulations and the molecular structures of volatile compounds. This training enables GAT to decipher the subtle relationships between different molecules and predict how they will interact to create specific aroma profiles.

The model employs a dual-system transformer network comprising an encoder and a decoder. The encoder processes the user’s prompt (with inputs such as top note (cherry candy), middle note (vanilla) and bottom note (cherry)), capturing the desired aroma profile. This is passed to the decoder, which generates a corresponding sequence of tokens representing the fragrance’s molecular structure.

GAT leverages the atomic structure of volatiles to generate novel formulations. Each molecule is represented as a graph, with atoms described by valence, degree, hydrogen count, hybridization, formal charge, and atomic number. These details are then translated into numerical representations and fed into a Graph Neural Network (GNN) model, which creates a unique vector representing each molecule. Similar vectors indicate similar molecules, allowing GAT to identify and utilize molecular structures with desired aromatic properties.

The potential impact of NotCo’s GAT is substantial. Developing new formulations for flavors and fragrances has traditionally been a time-consuming and resource-intensive process, often requiring weeks or months of expert work. If GAT can achieve the same outcome in mere seconds, it could significantly reduce flavor and fragrance development costs.

If you’d like to learn more about NotCo’s new generative AI tool for developing flavors and fragrances, NotCo’s head of machine learning, Francisco Clavero, and one of their key flavor and fragrance scientists, Cindy Sigler, will be our guests at the next Food AI Co-Lab on October 17th. You can register for this virtual event here.

From Data-Scraping to Discernment Layer: How NotCo’s Giuseppe AI Has Evolved Over the Past Decade

Almost a decade ago, while others experimenting with AI focused on algorithms for trading, diagnostics, or digital advertising, a company called NotCo was experimenting with AI by the name of Giuseppe to create plant-based foods that could match the taste and texture of their animal-based counterparts.

According to Aadit Patel, SVP of AI Product and Engineering at NotCo, the company’s founders (Patel would join a couple of years after the company was founded in 2015) realized early on that, in order to build an AI model that could help create plant-based products mimicking the taste, texture, and functionality of their animal-based counterparts, they would need a whole lot of data.

The problem was, as a startup, they didn’t have any.

When I asked Patel in a recent interview how the company overcame the infamous “cold start” problem—the challenge many embryonic AI models face before they have built large datasets on which to train—he told me they found the solution in a very public place: the U.S. government’s website.

“In the early days, when we had no money, we literally scraped the USDA website,” said Patel. “If you go to the USDA website, there’s a bunch of free data materials for you to use. And I guess no one had actually joined it together to create a comprehensive dataset… So the first versions of Giuseppe were built on that.”

This cobbled-together dataset formed the foundation for Giuseppe’s recommendations, leading to the creation of products like NotMilk, which uses unexpected combinations like pineapple and cabbage to replicate the taste of dairy milk.

As NotCo grew, so did Giuseppe’s capabilities. New analytical labs in San Francisco and Santiago, Chile, gave the company a wealth of new data on which to train its AI. Over time, the model’s ability to create innovative food products also improved.

One of the biggest hurdles in food development is the fragmented nature of the supply chain. Data is scattered across various entities—ingredient suppliers, flavor houses, manufacturers, and research institutions—each holding critical information that contributes to the success of a product. Over time, the company realized that to create an AI capable of building innovative products, it couldn’t rely solely on NotCo’s datasets. Instead, Giuseppe would need to integrate and analyze data from across this complex web of partners.

“What we’ve done with Giuseppe is figure out a way to incentivize this very fragmented ecosystem,” Patel said.

According to Patel, pulling together these disparate datasets from across the product development and supply chain would result in a more holistic understanding of what is needed for a successful product that is better aligned with market realities.

“We realized that if we just made an AI system that’s specific to CPG, we’d be losing out,” said Patel.

Generative AI and Flavor and Fragrance Development

One recent expansion of Giuseppe’s capabilities has been the exploration of new flavors and fragrances using generative AI. While GenAI models like ChatGPT have become infamous for creating sometimes strange and off-putting combinations when designing recipes and new food product formulations, Patel explained that the company has been able to overcome issues with general LLMs by creating what he calls a discernment layer. This layer filters and evaluates the multitude of generated possibilities, narrowing them down to the most promising candidates.

“Discernment is key because it’s not just about generating ideas; it’s about identifying the ones that are likely to succeed in the real world,” Patel said. “With generative AI, you can prompt it however you want and get an infinite amount of answers. The question is, how do we discern which of these 10,000 ideas are the ones most likely to work in a lab setting, a pilot setting, or beyond?”

The discernment layer works by incorporating additional data points and contextual knowledge into the model. For instance, it might consider a formulation’s scalability, cost-effectiveness, or alignment with consumer preferences. This layer also allows human experts to provide feedback and fine-tune the AI’s outputs, creating a process that combines AI’s creativity with the expertise of flavor and fragrance professionals.

Early tests have shown positive results. When tasked with creating a new flavor, both the AI and the human perfumers receive the same brief. When the results are compared in A/B tests, Patel says the outputs of Giuseppe’s generative AI were indistinguishable from those created by human experts.

“What we’ve built is a system where AI and human expertise complement each other,” said Patel. “This gives us the flexibility to create products that are not just theoretically possible but also market-ready.”

CPG Brands Still Have a Long Way to Go With AI-Enhanced Food Creation

Nearly a decade after building an AI model with scraped data from the USDA website, NotCo has evolved its AI to create new products through a collaborative approach that results in a modern generative AI model incorporating inputs from its partners up and down the food value chain. This collaborative approach is being used for internal product development and third-party CPG partners, many of whom Patel said approached the company after they announced their joint venture with Kraft Heinz.

“Ever since our announcement with Kraft Heinz and signing a joint venture, there’s been a lot of inbound interest from a lot of other large CPGs asking ‘What can you do for us?’ and ‘What is Giuseppe?’ They want to see it.”

When I told Patel I thought that big CPG brands have come a long way over the past twelve months in their embrace and planning for using AI, he slightly disagreed. He said that while there’s a lot of interest, most big brands haven’t actually transformed their business to fully create products with the help of AI.

“I would say there’s strong intent to adopt it, but I think there hasn’t been put forth like a concrete action plan to actually develop the first AI-enabled R&D workforce,” said Patel. “There is room, I think, for new AI tech for formulators, and room for best practices and lessons learned of adopting AI.”

You can watch my full interview with Aadit below.

The NotCo team will be at the Food AI Summit talking about their new efforts using generative AI to develop flavor and fragrance, so make sure to get your tickets here.

NotCo's Aadit Patel Talks About the Evolution The Company's Food AI Giuseppe

NotCo Built a Unicorn Using AI To Accelerate Food Innovation. CEO Matias Muchnick Tells The Spoon How They Did It

When Matias Muchnick started NotCo in 2015, food innovation was a slow-moving process.

“Food R&D was three guys in lab coats, doing trial and error in a developmental kitchen,” said Muchnick in a recent interview with The Spoon. “Reading research papers from 1980 about using soy to replace animal-based ingredients. That was it. So whenever you have an industry that has a very obsolete technology, then a lot of bad things happen.”

He and his co-founders wanted to create new plant-based food products, but they wanted to do it in a new way that didn’t rely on antiquated methodologies. Eventually, they started wondering if using technology like artificial intelligence could help them make better decisions and help create new types of food faster.

They decided yes and started building an extensive database of information about all the components that create the taste and experience of food.

“Your machine learning will always be directly proportionate to the amount of data and the dimensions of data that you collect,” said Muchnick. “So from the very beginning, understanding what data was relevant for the objective that we were trying to do, which was replacing animals with plants, was important to us.”

Their database was comprised of chemical and biological attributes that made food what it is and the attributes that impacted the human perception of taste, texture, smell, and color. The goal, said Muchnick, was to create a large enough database of information to use their AI (which would eventually be called Guiseppe) to create a whole line of new plant-based food products.

“We wanted to build a general-purpose artificial intelligence,” he said. “Not an algorithm that is only great at doing mayo, or a burger, or yogurt. The things you’d like from a burger are very different from what you’d like from yogurt, so (we wanted to know) how we could get a real understanding of the human brain to create an algorithm that would attack all of the categories of products. That was super important from the get-go.”

Seven years later, his company is an alt-protein unicorn: the company is growing very fast in the North American alt milk category, just started a joint venture with a food giant, and this week debuted a new alt-meat product at the natural foods show in Los Angeles.

So how does the platform that made all that possible work?

Muchnick gives an example of how the process would work if Giuseppe were used to, say, make a new kind of cheese.

“The algorithm comes up with this crazy amount of recipes and processes attached to each ingredient that we put into it,” said Muchnick. From there, they would take recipes and take them to the “AI test kitchen,” where a group of fifteen chefs try the product out, make the recipe, and then have it evaluated by a trained panel.

“The trained panel gives feedback to the algorithm. Maybe the formulation was good or bad, we feedback the algorithm with the good things and the bad things. So we feedback the algorithm with the many dimensions of the sensory aspects.”

Muchnick says its this continous loop where AI-generated concepts, recipes, and processes are tested in a kitchen, critiqued with feedback, which is then fed back into Guiseppe, which helps NotCo’s AI get better and better.

“You get an algorithm that is working on improving every single day with 1000 recipes.”

But it’s not just recipes getting better, but the optimization of processes around which they run experiments. Muchnick gives the example of a project on frothing plant-based milk. Instead of spending months experimenting with different ways to achieve it, Muchnick says it will help show faster routes to success to help deliver results in a week.

“Instead of starting from scratch with every food formulation you want to create, or any expression you want to create, the AI is telling the food scientist to go through different routes. The algorithm is optimizing every single set of experiments.”

And its this process and the success ultimately drew in Kraft Heinz to consider working with NotCo.

“Kraft Heinz said, guys, you do food products in a quality we’ve never seen before, at a pace we’ve never seen before, and with an agility and an execution that we haven’t seen before,'” said Muchnick. “‘How do you guys do it, and how can we partner up?'”

The answer to that question would eventually be a joint venture.

“They were like, ‘Why don’t we bring superior plant-based products with the familiarity of our brands and with your know-how of executing amazing R&D based products?'”

“And,” said Muchnick, “we’re like, ‘Yeah, I mean, that makes a ton of sense.'”

If you’d like to hear my full conversation with Muchnick about how they are using AI to accelerate food development, just click play below.

Kraft-Heinz and NotCo Form Joint Venture for AI-Powered Food Products

Today Kraft-Heinz and NotCo, the food tech company behind the NotCo brand of plant-based foods, announced they are forming a joint venture to develop a lineup of plant-based food products.

According to the announcement, the new company will leverage the strengths of both companies to develop and bring to market a new line of plant-based products. Called The Kraft Heinz Not Company, it will leverage NotCo’s patented AI platform to develop the food products, while Kraft-Heinz will offer up its production capabilities and formidable sales channels to help bring the products to market.

In joining forces with NotCo, Kraft-Heinz is partnering up with one of the hottest new brands in the fast-growing alt-milk category. The Chilean-based startup has secured distribution deals with a number of premium natural and organic food retailers such as Whole Foods, Sprouts and others since entering the US market in late 2020. The deal also gives the CPG stalwart access to the startup’s patented AI product development platform.

And its this AI platform, which goes by the name Guiseppe, which NotCo cites for its fast success in the US market. Guiseppe works by sifting through huge datasets from the US Department of Agriculture’s (USDA) National Agricultural Library and other sources to find ingredient and processing combinations that would best mimic the elements (flavor, texture, etc.) of real meat or dairy in plant-based analogues. The goal is to find the types of combinations that can create a product that completely mimics traditional meat and dairy — a feat few if any plant-based protein-makers have yet to achieve.

For NotCo, which has seen bigger CPG brands like Danone attempt to mimic its playful and somewhat irreverent product branding, the JV marks a potentially powerful new way to reach a broader swath of consumers in an increasingly crowded alt-protein market. However, by launching an entirely new set of products into the market with Kraft-Heinz, NotCo also runs the risk of potential cannibalization of its existing product lines. The deal announcement doesn’t specify how the new JV will sort out how the two will divvy up retailers, something that could come into play since some retailers will not have shelf space for similar alt-milks from the two brands. It could be that the Kraft Heinz Not Company will focus on new products such as plant-based cream cheese or other categories that play to Kraft’s strengths.

Finally, one wonders if this new JV will set a template for other large CPG brands looking to rejuvenate their product lines as more consumers turn to plant-based diets. Many of the old-school brands are ill-equipped to utilize newer product development tools like AI to create new products, so it makes lots of sense to partner up with ascendant brands well-versed in faster digital-cenric product development methodologies.

I Tried NotCo’s A.I.-Generated Milk Alternative

NotCo, a Chile-based company, is sometimes referred to as the Impossible Foods/Beyond Meat of Latin America. The company produces various alternative products including plant-based mayo, burgers, ice cream, and milk. When they reached out to me recently offering to send samples of their products, I happily accepted.

NotCo uses its patented A.I. platform to determine what plant-based ingredients would best replicate properties found in animal-based ingredients. For example, in its alt-milk products, the two main ingredients are simply water and pea protein. However, there is a small amount of pineapple juice, cabbage juice, sunflower oil, and chicory root, among other natural flavors, added to the milk alternative to bolster its flavor and texture.

The Bezos-backed company sent me a half-gallon of its whole milk and 2 percent milk alternatives. I first poured a small glass of the whole milk and drank it straight up. At first sip, I was surprised by the sweetness of it. Surprisingly, there are only three grams of sugar in a single serving. It had a vanilla flavor with a light aftertaste of coconut. The two percent milk had the same flavor, but tasted a tad bit more watery than the whole milk.

Photo of the alternative whole and two percent milk that was sent to me

I heated some of the whole milk in a pot on the stove and attempted to produce foam with my handheld frother. I had no luck, and the milk was completely flat after frothing for about two minutes. In defense of NotCo, they do not make any claims about their milk’s ability to froth, and I do not have barista-grade equipment. It makes me wonder if the company has plans to develop a “barista version” of its milk that is meant to produce a thick froth for lattes, like many alternative milk companies have done.

NotCo’s whole milk after being heated and frothed

I haven’t had milk for about eight years, but for what it’s worth, NotCo’s NotMilk reminded me of real dairy milk. The sweetness of NotMilk was reminiscent of the sweetness that lactose provides in dairy milk. However, there was something about the NotMilk that still suggested it wasn’t actually dairy. While I couldn’t quite pinpoint the reason, it could very well be the slight aftertaste/mouthfeel of pea protein. Overall, though I would say that NotMilk is a pretty good milk alternative product.

Pea protein, meanwhile, is a popular ingredient in the plant-based space due to its neutral flavor, versatility, and high protein content. It is the second ingredient in NotMilk, and both the whole and 2 percent milk contain four grams of protein in a single serving (one cup). U.S.-based Ripple and Swedish-based Sproud are two other companies that both also produce alternative milk made from pea protein.

NotCo launched its milk in the U.S. last year, and it seems like Oatly will be one of its biggest competitors in the alternative dairy space. Oatly has steadily gained a cult-like following in the U.S. since launching in independent coffee shops in 2016. The Swedish-based company went public this March, and raised $1.4 billion USD through this.

In the U.S., NotCo’s milks are available in stores like Whole Foods, Sprouts, and some independent grocers and retailers. A half-gallon of the alternative whole or two percent milk normally retails for $4.99.

Bezos-Backed NotCo Raises $235M for Plant-Based Alternatives

NotCo, a Chile-based food tech company that produces various plant-based alternative foods, announced today that it has raised $235 million in its Series D round of funding.

The round was led by Tiger Global and saw participation from DFJ Growth Fund and ZOMA Lab, with individuals also joining including Jack Dorsey, Joe Gebbia, Lewis Hamilton, Roger Federer, and DJ Questlove. Existing investors include Bezos Expeditions, EHI, Future Positive, L Catterton, and Kaszek Ventures. This brings the company’s total funding to $360 million.

This new capital will allow NotCo to expand into new product categories in North America and scale its proprietary A.I. platform. Additionally, the funds will help the company accelerate its plans to launch in Europe and Asia. Currently, NotCo offers five products: NotMilk, NotBurger, NotIceCream, and NotMayo. The products are available in approximately 6,000 retailers and foodservice locations throughout the U.S., Chile, Brazil, and Argentina.

Something that sets NotCo apart from other plant-based companies is its use of its A.I. technology (the company has five patents in the U.S. for this). Called Giuseppe, the proprietary A.I. platform analyzes the properties of thousands of plants in a database and then creates unique combinations with the goal of replicating animal ingredients. For example, the ingredients in the NotMilk product include pea protein, pineapple juice, chicory root, coconut oil, and cabbage juice.

NotCo joins the ranks with other large players in the plant-based space that have successfully expanded internationally. Beyond Meat fortified its presence in Europe earlier this year, and around the same time announced that it had opened a manufacturing facility in China. Impossible Foods and Just Eat made major expansions to Asia in the fall of 2020. Oatly is currently building or planning future production facilities in Singapore, China, and the UK.

In the U.S., NotCo’s NotMilk is currently available in Sprouts, Whole Foods, Wegmans, and other retailers. All of the company’s products are available in Chile, Brazil, and Argentina. By the end of 2021, NotCo aims to have its products available in 8,000 retailers globally.