By: Andy Triedman

The urban population of the world has grown from 751 million in 1950 to 4.2 billion in 2018 and is expected to increase further to 6.3 billion by 2050, representing more than two-thirds of the world population.

Global urbanization should drive economic opportunity, but the rapid rate of change has been breaking city infrastructure, and one of the areas that has been hardest hit is transportation. In 113 cities around the world, drivers lose more than 100 hours per year stuck in traffic. 98% percent of our 1,800+ mass transit systems lose money — about a dollar on each ride taken in New York, Boston, Chicago, and Washington; upwards of $4 in Dallas, New Jersey, Pittsburgh, and Virginia.

This already massive challenge is made immensely more difficult for public agencies by a stunning lack of data. These agencies make broad guesses at how their residents travel based on paper surveys sent to 1 in every 200 people and updated once a decade. This gives them frighteningly imprecise information to make critical decisions around transportation investments, policies, and tolls, let alone understanding and managing the impact of Ubers, Airbnbs, or scooters, whose use has exploded (likely years after the most recent data the agency has).

When we first met Nick and the Replica team at Sidewalk Labs early this year, we could immediately tell that the technology they were developing would make a night-and-day difference for public planners, analysts, and policymakers. Using modern data sources and computational/statistical techniques, they have built a modeling tool that allows these users to understand how, when, and why their population travels. This data is more granular and accurate than ever before and updated quarterly versus once in ten years to capture emerging trends and seasonal patterns. Soon, Replica will release a tool allowing cities to look forward and simulate potential scenarios — extending a rail line, adding a bus stop, building a new mall — to evaluate their impact for more informed decision-making. After less than two years, Replica is already working with major cities, states, and countries around the world.

The positive impact Replica will have on daily lives is far-reaching and long-lasting. However, the underlying location data required to build these models is one of the most personal liberties that we have, and it is imperative that any platform provides an absolute guarantee on preserving that privacy. Replica’s technology is based on the academic work of co-founder Alexei Pozdnoukhov, a pioneer in privacy-preserving methods for estimating location. From the start, Replica never touches original, identifiable data. The company then uses its de-identified data sources to generate a fully synthetic population — a new group of made-up residents that match the real world’s behavior in aggregate but do not correspond to any real person. We believe that Replica will set a new standard for how the government can use data to improve the lives of its residents while preserving their privacy and personal liberty.

The careful consideration that went into privacy and security exemplifies the thoughtfulness, dedication, and passion for doing good that drives Nick, Alexei, and the rest of the Replica team. We were honored to lead their Series A fundraise and help with their spin-out from Alphabet. We are immensely excited to partner with them to change the world of transportation. And if it takes 10 minutes off our daily commute, we won’t complain!

Announcing our Investment in Farmer’s Fridge

We live in the most obese nation in the world. 38% of American adults (aged 15 and over) are obese, and that rate is expected to increase to 47% by 2030. It’s a costly problem: an obese person annually spends an incremental $2,741 on medical costs, and on a national level obesity drives $200 billion or ~20% of total US healthcare costs. We eat more than we used to — about 24% more calories than in 1961 — but that’s only part of the story.

For decades, innovation in food processing and artificial ingredients has lowered costs and increased shelf life, enabling the distribution of massive amounts of packaged food. Research shows that 58% of the calories Americans eat come from these “ultra-processed” foods such as soft drinks, packaged snacks, mass-produced breads, reconstituted meat products, and instant noodles, while only 30% comes from minimally-processed food such as fruits and vegetables, grains, meats, and eggs. These ultra-processed foods have higher levels of sugar, fats (including saturated and trans fats), sodium, and lower levels of protein, fiber, and other micronutrients; consuming so much of them is a major driver of obesity and nutritional imbalance.

Vending machines are emblematic of the ubiquity of unhealthy food. America’s vending industry earned $22 billion in 2017, and operates over 2 million vending machines throughout the country. Well over 90% of the sales from these machines come from sodas and other sugary beverages, candy, packaged snacks and pastries — exactly the foods that increase the risk of obesity.

US farms grow as much fresh produce as they ever have, but the cost and complexity of creating a safe and agile supply chain for perishable food makes centralized models expensive and broad distribution entirely infeasible — about 33 cents of each dollar spent on food goes toward processing, packaging, and transportation, and fresh produce often spends up to 50% of its shelf life in transit between supplier and retailer. If you’ve ever tried to get fresh food at a highway rest stop, a secluded office park, a modest airport terminal, or one of the 24% of US ZIP codes that qualify as food deserts, you’ve likely experienced this problem first-hand.

What if we could change the 2 million vending machines to serve healthy, freshly prepared food — essentially creating the most convenient and nutritious restaurant in the world? To do so would require a new type of supply chain that changes the cost equation and enables broad, just-in-time distribution of fresh produce. For the first time, this is possible — leveraging emerging technologies, particularly advances in data science, compute, and engineering, to provide a convenient, fresh food experience to anyone, at an affordable price.

This vision is exactly what drives Farmer’s Fridge, serving restaurant-quality fresh and healthy salads, bowls, and sandwiches out of more than 180 automated smart Fridges across the Midwest (for now). While the experience at the Fridge is seamless, the back end is massively complex: Farmer’s Fridge collects millions of data points and uses them to drive a continuous optimization both of supply and demand. On the supply side, the data is used to predict consumption and adjust the supply chain daily, driving operational improvements from kitchen to delivery and decreasing waste. On the demand side, Farmer’s Fridge receives real-time feedback to iterate on new menu items in days vs. years and learn what type of food to prepare, for each day and each person.

The learnings that Farmer’s Fridge leverages are not one-time; using a robust data infrastructure, machine learning, and automation, the team can constantly experiment and drive agile development of both hardware and software. In a world where less than 5% of vending machines have systems to track inventory, the Farmer’s Fridge team is light years ahead of the pack and makes full use of technology innovation and decreasing costs in IoT and hardware.

The impact of this approach, while it may seem subtle, unlocks a paradigm shift in the business model of fresh food. By accurately matching supply to demand, Farmer’s Fridge can deliver fresh food to each Fridge daily, and economically serve the millions of locations with a desperate need for healthy food that might have a vending machine (or two) but don’t have enough traffic to support the overhead of a restaurant.

We’re excited to partner with Luke and the Farmer’s Fridge team, and lead this $30 million round alongside new and old friends at Finistere Ventures, Cleveland Avenue, Great Point Ventures, Dovi Frances, and others. We share the team’s passion for driving transformative change in an underserved market with data, compute, and engineering, and their vision to make restaurant-quality, fresh, healthy food as accessible as a candy bar.

Announcing our Investment in Afresh

By:Andy Triedman

In 2014, Innovation Endeavors started Farm2050, an ecosystem of industry leaders throughout the food and agriculture value chain, to tackle the immense and existential problem of feeding the world’s population of 10 billion by 2050. How can we drastically increase food production when we’ve already got all the land this earth will offer us (in fact, the world has lost about 33% of its arable land over the past 40 years)? In emerging markets, where there is a relatively lower adoption of modern agricultural technologies, we can try to boost productivity with fertilizers, irrigation systems, and crop protection products. While certain crops could see additional productivity gains from being grown vertically indoors, as is the vision of Innovation Endeavors portfolio company Plenty, others are likely near their full potential production.

We might not actually need to grow much more food to feed a burgeoning population. Instead, we might simply reduce waste, which currently makes up about 40% of all the food we grow. At which point in the supply chain this waste is created varies by geography. In emerging markets, waste is more likely to occur on the field, during harvest, or in transportation, but in countries like the United States, over 60% of food waste occurs at the end consumer.

What causes so much food waste? Sure, our restaurants should serve smaller portions and you should eat that last bite of pizza crust (…or should you?). But perhaps a more significant driver of waste, particularly for fresh food, comes from the supply chain. Supply chains for fruits, vegetables, meats, and dairy are often long, slow, and inefficient; a tray of berries might have had only a week of edible life when they hit store shelves leaving only a day or two by the time they land in your refrigerator. With such little time to work with, it’s no wonder that we throw so much food away.

Before you go rushing to your nearest grocery store with a pitchfork, know that they want to solve this problem too, maybe even more than you do. A typical supermarket operates with razor-thin margins — on average around 2.5% in net profit, one of the ten least profitable industries in the US — and the value of the food they have to throw away is often equivalent to or greater than their entire profits. Unfortunately, it’s not an easy fix. Predicting supply and demand is already a challenging task, and in fresh food, the forecasting problem is orders of magnitude harder. Fresh food often doesn’t have bar codes so it is frequently mis-scanned, is measured by weight but loses mass due to water evaporating, and might get tossed without record if it falls onto the ground (or into a devious shopper’s mouth). Every morning, in every grocery store in the country, a store employee walks the aisles with a clipboard and pen and struggles with these problems — eyeballing, approximating, and guessing how much of each item to order.

When Matt, Nathan, and Volodymyr met at the Stanford Graduate School of Business, they hoped that modern machine learning tools might offer a better solution to this wasteful and costly problem. They set out to research the opportunity in an independent study project sponsored by Scott and, after finding success, formed Afresh. Innovation Endeavors participated in the company’s Seed round and today we are thrilled to publicly announce that we have doubled down to lead their Series A fundraise and accelerate the company into broader deployment.

Afresh plugs into supermarkets’ inventory and POS systems, as well as external data sources, to assemble a detailed understanding of fresh food moving into and out of each store. They then leverage cutting-edge, reinforcement learning-based machine learning algorithms, factoring in supply chain and logistical constraints, to recommend the precise quantity of each item a store should order each day. Store clerks follow their same workflow as always, but with a tablet instead of a clipboard, and order boxes that magically autofill with suggestions (that clerks can override). The results? For the clerk, less time spent ticking boxes and more time on the floor with customers. For the store, less waste (a 25–45% reduction for initial customers) and fewer stock-outs, meaning a massive impact on the bottom line. And for consumers, fresher food that will taste better and last longer on the shelf or in the fridge. With such a meaningful effect on both supermarket economics and environmental impact, Afresh has seen rapid traction, currently deploying in three major grocers across the country.

In the longer term, Afresh has its sights set not just on grocery store shelves, but on all the players that handle food — distributors, producers, restaurants, and more. By deploying its advanced machine learning platform broadly, Afresh aims to help the entire value chain be less wasteful and more profitable while providing healthier and fresher food to all.

We’re thrilled to partner with Afresh to solve some of the most costly and wasteful problems that face our food system. If you’re another team leveraging technology to drive transformational efficiencies in supply chains, we’d love to hear from you!