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AI

July 1, 2025

From Red Bull to Zevia, Amy Taylor Shares Lessons Learned From a Career Built Around Buzzy Beverages

In the early 90s, Amy Taylor had dreams of Olympic gold as an elite track and field athlete.

Back then, she never could have predicted she’d spend the bulk of her career in the beverage business. But after moving to Atlanta (where the Olympics were to take place in 1996) and working for a short time for the Atlanta Hawks, it wasn’t long before she started to work for Red Bull, just as the now-famous brand was beginning to define the energy drink business in the early 2000s.

“My stepdad warned me not to take the job because he had never heard of the company,” she recalled. “And I said, I think there’s something special here. I took my Gen X assignment of creating this Red Bull brand with an American lens on it for the American audience.”

Taylor would spent over 20 years at Red Bull, eventually serving as president and chief marketing officer, where her time there shaped her philosophy on building iconic brands.

“What I learned there was about creating a hot brand and sort of becoming a part of or creating communication within and around the zeitgeist,” she said. “Instead of trying to go fast and hard and drive distribution and awareness at all costs… the brand was building relationships.”

Now, as CEO of Zevia, Taylor is applying those lessons to a different kind of beverage mission. “We are going to materially reduce sugar consumption among the population that we serve,” she said. “If a family switches from carbonated soft drinks to Zevia, they can cut their sugar consumption in half with one move.”

Known for its zero-sugar sodas made with stevia, Taylor says Zevia aims to provide an affordable, clean-label alternative for families. She’s also focused on evolving the brand’s taste and product innovation. “There are 20 molecules in the stevia leaf that can sweeten a product,” she said. “Our job is to go extract the ones that perform best within the beverage. For the part of the population that have had a negative experience with stevia, they’re going to need to come back and try Zevia. And I think they’re going to be blown away.”

Like most food brands nowadays, Zevia is embracing AI. Taylor says they are doing it with a “hacker’s approach,” which means encouraging every department to experiment with new use cases.

“Each department head challenges their entire team, not just their senior leadership, to come up with new use cases for AI,” Taylor said. From creating digital consumer prototypes to enhancing operations and finance workflows, Taylor said the company is exploring numerous applications. “We use AI to challenge our thinking and our assumptions. We want to grow faster because of our ability to leverage AI with the people that we have in the building today.”

Part of Taylor and Zevia’s push to leverage innovations like AI is because the company operates lean (fewer than 100 employees), and new technologies can help them punch above their weight.

“We are small and focused,” she said. “And we are scrappy as hell.”

You can listen to my full conversation with Taylor below and can connect with her (and ask her questions) at the Smart Kitchen Summit later this month.

June 30, 2025

Study: AI-Powered Drones Fuel Advances in Precision Ag for Early Detection of Crop Stress

Early stress detection via precision agriculture just got a serious upgrade, according to new research out of the Hebrew University of Jerusalem. Led by Dr. Ittai Herrmann, the team developed a drone-based platform that blends hyperspectral, thermal, and RGB imaging with powerful deep learning technology to precisely identify nitrogen and water deficiencies in sesame crops.

Sesame, known for its resilience to climate variations, is rapidly growing in global importance. However, accurately identifying early-stage crop stress has historically posed a significant challenge, limiting the ability of farmers to respond quickly to potential catastrophic challenges. To tackle this, the researchers combined three advanced imaging technologies into a single drone system, creating a robust solution capable of decoding complex plant stress signals.

Hyperspectral imaging provides detailed spectral insights into plant chemistry, including nitrogen and chlorophyll levels, which are critical markers for plant nutrition. Thermal imaging spots subtle temperature changes in leaves that indicate water stress, while high-resolution RGB images provide clear visual context of overall plant health and structure.

What made this study cutting-edge was its use of multimodal convolutional neural networks (CNNs), an advanced AI approach that can unravel intricate data patterns and add context, which significantly enhances diagnostic precision. These advanced techniques unlocked the researchers’ ability to distinguish overlapping signals of plant stress, such as differentiating between nutrient and water deficiency, something that conventional methods often struggle to achieve. According to the researchers, by accurately pinpointing the exact stressor, farmers can now apply resources such as fertilizer and irrigation more strategically, reducing waste and environmental impact while increasing crop yields.

While other researchers have studied using advanced AI techniques with drones to aid in combatting stress in walnut and specialty crops, the use of deep multimodal CNN appears to be a leap forward in precision ag. It remains to be seen how quickly this technology reaches the farmer level, but given the challenges of climate change, its easy to envision that these types of advances in precision agriculture will be invaluable tools for farmers in the future to protect against climate-related crop stress.



June 25, 2025

Leanpath CEO: The Fight Against Food Waste Enters Its ‘Second Act’

At this year’s ReFED Summit in Seattle, Andrew Shakman was in a reflective mood. When asked about the state of the food waste reduction movement, the Leanpath CEO, who has spent more than two decades fighting food waste, compared the moment we’re in to the second act of a three-act play.

“If we go back to Aristotle’s Poetics, the beginning is always gripping. The middle is hard, and I think we’re in act two, where it’s no longer the beginning, but we are not at the end.”

According to Shakman, it’s a moment of transition for the food waste movement. The early momentum that defined the last decade, fueled by sustainability pledges, bold 2025 goals, and a wave of startup innovation, is giving way to a more complicated reality. Some organizations are hitting their targets while many are falling short. And now, for many fighting the fight, the question isn’t just what the next goal should be, but how to keep the movement going.

According to Shakman, that means leaning into the business case for food waste reduction, much as he did when the company first started.

“When we started, I’ve been at this 21 years. It was all about money. It was all about saving money, pulling dollars out of the garbage,” he said. “We came to understand that this was a nexus issue that incorporated issues around climate and food security and land conversion and biodiversity and water resources and everything. Today we’re reverting back to a narrower story that’s more focused on business case simplification, making life as business-focused as possible, because of the environment we’re in right now. There’s less political unanimity around climate and ESG.”

Shakman believes this re-focusing on the business case is happening because many enterprises are deemphasizing meeting sustainability goals, in part due to the political moment we are in in 2025. But just because the Leanpath CEO sees a powerful message in emphasizing efficiency and saving money as key motivators to adopt food waste reduction tech, he doesn’t think those in the industry should abandon talking about how important waste reduction is for the environment.

“You can still have the whole conversation,” Shakman said. “But the emphasis is on the business case at the moment. I don’t think we should allow ourselves to walk away from the moral imperative”

When I asked him about AI, he said the technology is most powerful when it drives action in the kitchen and elsewhere.

“Chefs did not get into food because they wanted to sit in front of their computer,” Shakman said. “They want to touch and make touch, make connect and create experience, and they want to know what’s the fastest path to taking the most impactful action, and that’s where I think AI is going to be very exciting.”

Shakman believes AI is most powerful when it adds context to decision making through triangulating different data sets, but believes the industry – and its data – is in many ways structured in a way that makes creating that contextual nuance difficult. The real breakthrough, he believes, will come from breaking down the data silos in foodservice technology.

“There are POS data assets for what you’ve sold,” said Shakman. “There are inventory data assets around what you bought and maybe what’s on your menu. There are now waste data assets that are actually unique contributors to the data landscape. And with those, when you triangulate with what you sold and what you bought, you now have the ability to see things that you could never see before.”

But even as Leanpath builds toward that integrated vision, one that blends frontline kitchen tools with enterprise-level oversight, Shakman remains focused on the people behind the data. “The changemakers on this issue are the people working in kitchens,” he said. “They’re driven by emotion, by the desire to do good. If you can align action with purpose, you unlock something powerful.”

Shakman’s framing of the food waste battle as a three act play isn’t all that surprising since storytelling runs in the family; his brother, Matt Shakman, is a longtime Hollywood director, directing shows like WandaVision and It’s Always Sunny in Philadelphia and the upcoming Marvel movie, The Fantastic Four: First Steps.

Both Shakman brothers are, in their own way, trying to navigate two very different Act Twos and shape what comes next.

You can watch my full conversation with Andrew from the ReFED conference below and find it later this week on The Spoon Podcast.

The Spoon Talks With Leanpath's Andrew Shakman at ReFED Summit 2025

June 12, 2025

Starbucks Unveils Green Dot Assist, a Generative AI Virtual Assistant for Coffee Shop Employees

While most companies across the food value chain are embracing AI in some form, one major player that’s been notably quiet is Starbucks.

From mobile ordering to Web3 experiments, and computer vision-powered bioauthentication to automated drink-making, the Seattle-based coffee giant has never shied away from tooting its own hard about tech-forward initiatives. But when it came to generative AI, the most hyped tech trend of the past few years, Starbucks had kept relatively quiet, leaving many to wonder what it was working on and when it might reveal its plans.

That wait is over. This week, at a 14,000-employee conference in Las Vegas, the company unveiled Green Dot Assist, a generative AI-powered assistant designed to help baristas and store managers streamline their operations.

So, what is Green Dot Assist? In short, it’s a Microsoft Azure-powered virtual assistant currently being piloted in 35 Starbucks locations. The app assists with a range of tasks, from training new employees on how to prepare specific beverages to supporting shift managers with dynamic scheduling in response to real-time changes, such as last-minute call-outs.

Green Dot Assist even troubleshoots hardware issues. In a demo video shared by Starbucks, a barista named Dave uses the assistant to diagnose an espresso machine that’s pulling inconsistent shots. The AI provides 3D visual guides and prompts Dave to submit a service ticket—an experience that blends visual diagnostics with conversational support.

Packaged in an iPad app (apparently, Microsoft couldn’t convince the coffee chain to use Surface devices), Green Dot Assist combines training, support, and efficiency tools, all powered by Azure’s generative AI capabilities.

Given Starbucks’ longstanding emphasis on employee training, an AI-powered employee training guide and assistant makes sense. But my guess is this is just the beginning. In the longer term, I expect Starbucks to leverage AI to further enhance operational efficiency, particularly given the significant shift in order mix towards mobile ordering, which has led to increased wait times and customer frustration. This next wave will likely include more advanced automation, as we’ve already started to see with the chain’s push to roll out its Clover Vertica machine nationwide this year and – possible – a new point of sale system announced this week at the company’s employee conference.

May 13, 2025

A Week in Rome: Conclaves, Coffee, and Reflections on the Ethics of AI in Our Food System

Last week, I was in Rome at the Vatican for a workshop on the ethical and social implications of artificial intelligence and automation in our food system.

The workshop was part of an ongoing three-year NIH-funded project focused on the ethics of AI in food. It took place at the Vatican’s Pontifical Academy for Life, the same institution that played a pivotal role in 2020 in getting Microsoft, IBM, and others to sign the Rome Call for AI Ethics, a cross-sector commitment to develop AI that “serves every person and humanity as a whole; that respects the dignity of the human person.”

I was invited to provide an overview of AI in the food system to help set the stage for the day’s conversations, which featured Michelin-starred chefs, Catholic priests, journalists, authors, and professors specializing in ethics, artificial intelligence, and more. I walked through some of the developments I’ve seen across the food system—in agriculture, next-gen food product development, restaurants, and the home. As I wrote recently for The Spoon, today “every major food brand has made significant investments — in people, platforms, products — as part of the AI-powered transformation.”

I posed questions like: What happens when AI dictates what we eat? Or if it engineers the “perfect sandwich”—something so addictive it floods demand and strains supply chains, as Mike Lee has imagined? What does it mean when a company builds a proprietary food AI trained on global culinary data? Does that dataset become the intellectual property of one corporation? And if AI can tailor nutrition down to the molecule, who controls those insights?

These are not just technical questions. They’re questions with deep implications for humanity.

One thing was clear throughout the day: everyone in the room recognized both the promise of AI as a tool for addressing complex challenges in the food system, and the risks posed by such a powerful, society-shaping technology. Among the questions raised: How do we balance the cultural and inherently human-centered significance of food—growing it, preparing it, sharing it at the family dinner table—with the use of AI and automation across kitchens, farms, and wellness platforms?

Above: The signed Rome Call for AI Ethics

As some attendees expressed, there’s a growing concern that the “soul” of food—its role in connection, tradition, and creativity—could be lost in a world where AI plays a central role.

For obvious reasons, being at The Vatican and in Rome at this time was a bit surreal, as the two days of the workshop and the Vatican came during the same week that the College of Cardinals gathered to select the next Pope after last month’s passing of Pope Francis.

As we wrapped up our discussions, the Conclave began. And just as I was leaving Rome, white smoke rose from the chimney of the Sistine Chapel, signaling that a new pope had been chosen.

In his first address, Pope Leo XIV made it clear that he is thinking deeply about AI’s role in society, so much so that he chose his name in homage to a previous pope who guided the Church through an earlier technological upheaval.

“… I chose to take the name Leo XIV. There are different reasons for this, but mainly because Pope Leo XIII, in his historic encyclical Rerum Novarum, addressed the social question in the context of the first great industrial revolution. In our own day, the Church offers to everyone the treasury of her social teaching in response to another industrial revolution and to developments in the field of artificial intelligence that pose new challenges for the defence of human dignity, justice and labour.”

Also present at the workshop was our friend Sara Roversi, founder of the Future Food Institute. The Spoon and Future Food Institute co-founded the Food AI Co-Lab, a monthly virtual forum where experts across disciplines explore the intersection of food and AI.

Sara, Tiffany McClurg from The Spoon, and I grabbed coffee at a small café in Rome to reflect on the workshop and what it means for our ongoing work. We launched the Food AI Co-Lab in early 2024 as a space to gather our communities and talk through how AI is impacting the food system. So far, much of the conversation has focused on education—helping people understand what AI is and how to thoughtfully implement it in their organizations.

But we all agreed: the world has changed rapidly since we began. Nearly everyone is now seriously considering how to integrate AI into their companies, institutions, or personal lives. And so, the Co-Lab needs to evolve too. Our hour-long sessions, often featuring guest speakers, have been great for tracking innovation, but now it’s time to elevate the conversation. Ethics. Labor. Equity. Sustainability. These aren’t side topics—they’re central to how AI will shape the future of food.

If the world feels more chaotic than ever, one thing is certain: we need to prepare for faster, more unpredictable change. At the first workshop two years ago, most attendees were just learning about AI. There was plenty of fear about a runaway system invading the food chain.

Today, there’s greater recognition that AI is inevitable and that it can be a powerful tool for solving some of the food system’s most complex problems. There was even a bit more optimism this time.

But above all, there’s a clear understanding that we still have a long road ahead to strike the right balance: embracing AI as a tool while preserving what makes food so deeply human, so critical to our culture, communities, and shared existence.

You can learn more about the Food AI Ethics project led by Cal Poly at San Luis Obispo [here]. If you’d like to join us for future Food AI Co-Lab events, you can sign up via our LinkedIn Group or The Spoon Slack. We’ll keep you updated on upcoming events and speakers.

May 6, 2025

How ReShape is Using AI to Accelerate Biotech Research

“Biology is so complex, it’s like the most complex piece of technology in the entire world,” said Carl-Emil Grøn. “There’s nothing that’s remotely close. You start from one cell and then you grow into a Michael Wolf who’s now hosting a podcast together with me. That is crazy when you think about it.”

When this former single-cell turned human podcaster caught up with the CEO of ReShape Biotech this past week on The Spoon Podcast, Grøn’s excitement over the miracle of biology and biotech was palpable. But he made it clear that wasn’t always the case. In fact, when he first saw his university friends building a tiny robot for a biotech professor, he told them it was a waste of time.

“I was sure this was not something anybody would need,” Grøn recalled. “But then I started getting a little bit curious about it.”

That curiosity eventually led him to co-found Reshape Biotech, a Copenhagen-based startup that’s automating the slow, manual processes still common in biological research. While new technologies like automation and AI have transformed fields like software and transportation, Grøn saw that many biotech labs were still stuck in the past.

“We have self-driving cars and AI tools that can do crazy things, but biotech workflows look kind of like 1990.”

Reshape’s platform combines robotics, computer vision, and machine learning to help food and biotech companies run hundreds of thousands of lab experiments. The ReShape system uses cameras to monitor petri dish experiments, running AI-powered image analysis on mold growth or bacterial reactions, and helping researchers rapidly test natural preservatives, food dyes, and more. This means research that once took months or years can now be done in days or weeks.

“We have this one company that used to do between like 800 and 1000 experiments per year,” Grøn said. “Whereas with our platform, they’re running more than 450 thousand every single year. So you get this like complete step change difference in how much you can actually do.”

That kind of increase in throughput is becoming more critical as food companies face new pressures, whether that’s from consumers demanding clean labels to a new administration looking to restrict artificial ingredients.

“Nowadays, they (food companies) are going to have to do it right,” he said. “When regulatory pressure comes to push, you have to do it.”

Grøn believes the companies that embrace AI and automation today will have a major advantage tomorrow.

“If we do this well, these companies will be set up to basically take the lead on developing new products in the future,” he said. “They will be the ones who have the data that’s necessary to make AI models that actually work.”

As a startup, Grøn says ReShape is focused on getting their tools into the hands of big players like Unilever and Nestlé, but long-term he has a broader vision, which is to open up the world of biotech data to help make companies big and small more productive.

“My dream, maybe one day, is to open source all of this data and just make it available to the world,” he said. “Because I do think the world needs something like this.”

Grøn was vague on when exactly that would happen, as he said first he has a few constituencies (like his investors) which he needs to serve first. But over the long term, he’s excited about the possibilities.

If you’d like to listen to my full conversation with Grøn, you can click play below, or find it on Apple Podcasts, Spotify, or wherever you get your podcasts.

The Future of Biotech Discovery in the Age of AI

April 28, 2025

Next-Gen Fridge Startup Tomorrow Shuts Down

Fridge startup Tomorrow will not live to see another day.

Last week, founder Andrew Kinzer cited the difficult funding environment for hardware startups and the headwinds around the uncertainty in tariffs in a post on LinkedIn.

I knew this would be a massive challenge. Consumer hardware is notoriously difficult, and solving shelf-life extension would require a scientific leap. I understood then that I could swing and miss, but I always felt that if I did, I could still be proud I gave it a shot.

In the end, though, timing is everything. Right now — maybe more than at any point in the past decade — consumer hardware is a tough sell for investors, and fluctuating tariffs only add more risk to the equation.

The company’s website also features a going-out-of-business message, citing the same reasons Andrew did in his post and thanking those who helped out along the way:

After much consideration, we’ve made the difficult decision to shut down Tomorrow.

When we set out to build a next-generation fridge—one that could extend the life of your fresh produce, reduce waste, and help make healthier eating easier—we knew we were taking on an ambitious challenge.

Unfortunately, the current climate for consumer hardware—especially for capital-intensive, science-forward products like ours—has made it incredibly difficult to bring something like this to life.

Though we won’t be moving forward, we’re deeply proud of the work we did and grateful for the community that rallied around our vision.

To everyone who signed up, supported us, or offered guidance: thank you.

When I first covered Tomorrow last year, I was admittedly excited to see a new company take a shot at reimagining such a moribund category. How we store food hasn’t seen nearly as much innovation around how we grow, cook, shop and make food, and so any new startup taking a shot was a good thing as far as I was concerned.

It’s hard to say whether Tomorrow would have succeeded if they had been able to raise funding, in part because I’m not sure exactly what the company’s key technology differentiation was. That’s because the company kept their product details close to their vest, pointing to its intention to keep fresh food fresher longer, leveraging AI and other technologies when asked about specifics.

I can also say I’m not surprised by the reasoning behind the shutdown. Hardware is a hard category to build a business in normal times. Throw in tariffs, which would no doubt complicate the supply chain and manufacturing strategy of a refrigerator startup, and significantly raise the final price of the product. Creating an entirely new product in this space almost becomes a fool’s errand, at least in the current environment (which is also probably why raising funding for this company proved extremely difficult).

April 28, 2025

From Starday to Shiru to Givaudan, AI Is Now Tablestakes Across the Food Value Chain

Back in the early days of the cloud computing revolution, my former employer, GigaOM, hosted perhaps the biggest and most influential conference on the topic called STRUCTURE.

One of the phrases that has stuck with me from those days is “data is the new oil,” which I heard declared from the STRUCTURE stage more than a handful of times. At the time, big data technologies were leveraging machine-learning-driven analytics tools to create new correlations and insights from disparate datasets faster than ever before. Those who controlled the data — and could mine it effectively — wielded enormous power.

Now, nearly two decades into the cloud era and three years after the AI “big bang” sparked by the launch of ChatGPT, those early days seem almost quaint by comparison. New AI-powered tools and companies are emerging every day. While much of the “data is the new oil” rhetoric back then felt like spin, today we’re seeing real, transformative progress, especially in new product development.

Food is no exception.

Take the news from Shiru this past week. The company, which uses AI to sort through plant-based food building blocks, announced that it had scaled its first AI-discovered products: OleoPro and uPro. These new approaches to identifying proteins — particularly oleogel structurants (structured fat systems) — are designed to support large-scale production.

As Shiru CEO Jasmin Hume put it:

“This moment is a turning point not just for Shiru, but for the food industry. Even though oleogels have been explored for years (there are over 500 publications on them in the last decade), commercially scaled examples have been elusive — until now. Our AI platform helped us identify the right proteins, but that was only part of the story. Our team then engineered a scalable and entirely new process for producing those proteins with the precise performance attributes required to succeed in real-world formulations.”

But it’s not just next-generation ingredient discovery. New CPG brands are also using AI to decipher early consumer signals and connect the dots before anyone else can launch the next big product. One example is Starday, a startup that recently raised $11 million. Starday uses AI to sift through millions of data points from social media feeds, surveys, point-of-sale data, and more to identify emerging opportunities in food that could lead to future hits.

“Imagine if you had 10,000 consumer insights folks that are watching every video on internet, typing up what’s being said, tagging it, and then kind of building these regression models around how these trends are happening,” said Starday CEO Chaz Flexman in a recent interview with The Spoon. “We’re trying to do that on steroids. We take in about 10 million pieces of content every week, which is very significant.”

In the early big data heyday, companies could look at things like trending tweet mentions. Today, companies like Starday are able to dive into video content, extract context much faster, and build predictive intelligence to guide new product development.

Shiru and Starday are just two examples making headlines recently about how AI is reshaping the food industry. Others are innovating across different parts of the food value chain — from manufacturing optimization (Keychain) to intelligent automation (Chef Robotics), all the way back to the farm with companies like Agtonomy.

Even century-old flavor companies are getting into the act. This past week, Givaudan announced Myromi, a handheld digital aroma delivery device that leverages an AI platform called ATOM.

In short, AI is enabling both startups and established players to move much faster.

And they’re going to have to. In the current MAHA moment in the US, companies are urgently reevaluating ingredient lists and being forced to replace ingredients like food dyes and sugars. This new urgency is adding to what many had already been doing as they see climate change slowly but surely impacting how and what they can source for their products.

Back in 2010, there was a lot of talk about using big data to create better products, but no one was seriously using AI to build food products at that point (heck, Watson, after all, hadn’t even become a chef.) Today, every major food brand has made significant investments — in people, platforms, products — as part of the AI-powered transformation.

In other words, if data is the new oil, it’s now clear that AI is the engine of innovation that is accelerating and driving change across every part of the food system.

April 17, 2025

Join Us Today as We Discuss How Artificial Intelligence Will Impact Culinary Creation

Admit it: you’ve probably played around with making recipes using AI. At this point, most of us have.

If you’re like me, the early results were… rough. But over time, general-purpose LLMs have become surprisingly good at whipping up recipes. Still, there’s a long way to go before AI becomes a true sous chef in our kitchens, and plenty of questions remain about where this is all going.

To help us explore what’s next in this month’s edition of our Food AI Co-Lab, we’re joined by two people who’ve been working at the intersection of AI and cooking for nearly a decade: James Briscione and Lav Varshney, co-creators of Chef Watson—the world’s first culinary AI. Their latest project, CulinAI, is an AI-powered app designed to create personalized meal plans.

Want to join the conversation, ask questions, and see where AI cooking is headed? Register for today’s Food AI Co-Lab here.

April 14, 2025

ClearCOGS Raises $3.8M its AI-Powered Forecasting Software That Helps Restaurants Reduce Waste

AI-powered restaurant forecasting startup ClearCOGS has raised $3.8 million in an oversubscribed seed round led by Closed Loop Partners, with participation from Myriad Venture Partners and Hearst’s Level Up Ventures. The funding includes $2.3 million in new capital and the conversion of $1.4 million in pre-seed investments. The company’s software provides predictive analytics to assist operators in making decisions around food prep, ordering, and staffing, with an emphasis on reducing food waste and improving operational efficiency.

Company CEO Matt Wampler told The Spoon that he came up with the idea of ClearCOGS during the pandemic. He’d been being laid off and was exploring coding and analytics, when he discovered his cousin who ran a Jimmy John’s franchise was still using a decade-old Excel forecast. Wampler wondered if AI could help create a better predicitve forecasting tool, and before long he had teamed up with Osa Osarenkhoe to build a solution that uses machine learning and time-series forecasting that currover 100 million data points a day.

When ClearCOGS participated in our first virtual Food AI Summit a couple of years ago, Osa and Matt had started experimenting with leveraging large language models (LLMs) like those from OpenAI to create an interface for their forecasting tool. I asked Matt how those experiments with LLMs had gone.

“We did a whole big thing with it… It didn’t go well,” said Wampler. “And it wasn’t from a technical standpoint. It was from the standpoint of the restaurant brands we were talking to… they were like, ‘Look, my general manager can either just get on and play with your AI bot and it’ll tell them, or you can just send it to them? Just send it to them.’”

Wampler said the LLM interface wasn’t the problem. It was just that operators didn’t want to interact with it at all. Instead, they just wanted the answers delivered to them, simply and directly, through email and integrations with solutions from Toast or SevenRooms. This experience reaffirmed Wampler’s belief that proprietary forecasting (and not LLM-powered conversational AI) is where ClearCOGS can deliver the most value.

“LLMs are kind of a commodity at this point. Proprietary data sets are what really matters… You still have to be able to provide a fundamental business value before that AI is really helpful.”

While many platforms offer dashboards or raw analytics, ClearCOGS focuses on delivering direct, decision-ready insights to restaurant managers. This is central to how he differentiates the company:

“If you’re a brand, you probably have 20 or 30 questions that you have to answer every day… We go really deep on those and provide a systematic way of delivering those to your operators every day.”

With the new capital, ClearCOGS plans to accelerate its product development and customer acquisition efforts, with an emphasis of better positioning itself in the food service sector. The company currently serves a customer base of 100 brands in four countries, and Matt says they plan to continue building a lean team, prioritizing automation and AI over headcount.

March 31, 2025

Food Assembly Robot Startup Chef Robotics Raises $43M Series A as it Reaches 40 Million Meal Milestone

Chef Robotics has raised $43.1 million in a Series A round to accelerate deployment of its AI-powered food assembly robots, the company announced today. The funding brings the San Francisco-based startup’s total capital to over $65 million, including equipment financing. Avataar led the round, with participation from Construct Capital, Bloomberg Beta, and others.

Founded in 2019, Chef Robotics is building what founder and CEO Rajat Bhageria calls an “AI platform for food.” Rather than building a single-purpose robot, Chef’s system is designed to work in diverse food production environments—learning and adapting through software to new tasks like portioning, topping, or filling.

When I first got a peek at Chef’s system last year, I was intrigued because the company had struck a balance that seemed to elude many food robotics startups. While startups in this space seemed to make either high-volume solutions with limited customizability or use off-the-shelf robotic arms that aren’t made for true high-production, Chef has built a flexible and scalable robotics platform that can be customized for any number of high-volume food production environments.

That’s because while many robotics companies focus primarily on hardware, Chef’s approach centers on a software layer that enables “Embodied AI”—giving physical robots the intelligence to operate autonomously in real-world conditions. Chef’s system combines a robotic arm with AI models trained on millions of real-world examples. These models, powered by production data from early customers like Amy’s Kitchen and Fresh Prep, allow the robots to generalize across new ingredients and dishes. To date, Chef Robotics has helped assemble over 40 million meals.

From the company’s announcement: When we thought about starting with restaurants, we ran into the chicken and egg problem – to enable robots that are flexible enough to add value, we need a highly capable AI, but to get a highly capable AI, we need real-world training data from the customer sites…. Thus, we decided to initially deploy robots in high-mix (read as highly flexible) food production and manufacturing environments where Chef could partially automate a food operation and thus add value in production to customers without requiring 100% full autonomy from the get-go. We built Chef’s systems on modern advancements in AI to make them highly flexible and adaptable enough to “pick” and plate almost any ingredient, no matter how it’s cut, cooked, or grown; this makes them an ideal solution for assembling or plating food.  

The new capital will support scaling up deployments and building out Chef’s sales and marketing teams. The company is currently active in the U.S. and Canada, with plans to expand into the UK next year.

March 28, 2025

The Food & Retail AI Rollup Continues as Crisp Buys Shelf Engine

Crisp, a New York-based retail data company, has acquired Seattle-based startup Shelf Engine. Founded in 2016 by Stefan Kalb and Bede Jordan, Shelf Engine specializes in using machine learning to optimize ordering processes for perishable goods, with the goal of reducing costs and minimizing food waste. The platform is now in use across more than 7,000 stores. Terms of the acquisition were not disclosed.

Shelf Engine was one of the earliest adopters of AI technology in food retail aimed at reducing waste and optimizing fresh food ordering. Kalb, who launched a food distribution business at 23 and holds degrees in actuarial science and economics, developed the idea during a 2014 ski trip with his friend and engineer Bede Jordan, a former Microsoft HoloLens developer. The pair questioned why food industry processes and systems remained outdated:

“Could we create a platform that enables retailers to buy food and eliminate significant waste? Could we create a platform that eliminates redundant busywork between vendors and retailers? Could we create a more perfect marketplace?”

These questions led Kalb and Jordan to develop a product designed to drive the food industry towards greater efficiency through technology.

Kalb reflected on the deal on LinkedIn:

“What started as a side project with my good friend Bede turned into a platform that’s now in over 7,000 stores across the U.S., helping reduce millions of pounds of food waste. It’s been eight years of wild highs, humbling challenges, and so much growth.”

Crisp plans to integrate Shelf Engine’s advanced algorithms into its commerce platform. The company believes the integration will help its retailer customers optimize in-stock inventory, improve shelf management, and drive revenue in an increasingly margin-sensitive retail environment.​

“Joining forces with Crisp allows us to scale our proven technology and deliver greater value to retailers and their supplier partners,” said Kalb. “Together, we will set a new standard in forecasting and inventory management, helping our customers thrive even in challenging market conditions.”​

This news is yet another in a series of acquisition announcements for early pioneers who are building technology leveraging AI to optimize different parts of the food value chain. Earlier this month AI Palette was gobbled up by trend forecaster Global Data, and before that Spoonshot was acquired by Target. Like other buyers in these deals, Crisp provides predictive intelligence software and services and is buying Shelf Engine to improve their AI insights capabilities.

Unlike these previous deals, Crisp and Shelf Engine focus more on retail and supply chain commerce optimization, which is one of the areas that is seeing the greatest leaps forward in productivity and cost-reduction. My guess is Afresh, which is similar to Shelf Engine and remains independent following its $115 million in series B funding in 2022, may also be one of the next companies gobbled up as bigger software and supply chain players look to add AI capabilities to their products.

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