Paul Breitenbach does not believe America has a food shortage problem, he believes it has a systems failure. Where nearly $400 billion in surplus food coexists with record levels of food insecurity, the Founder and CEO of r4 Technologies argues that the missing ingredient is not supply, but coordination. In this exclusive interview with TechBullion, Breitenbach outlines how cross-enterprise AI could fundamentally rewire the food ecosystem, transforming disconnected supply chains, food banks, retailers, and government agencies into a single predictive network capable of redirecting surplus food in real time. From moving 30 tons of perishables across eight states before spoilage to redefining AI as public infrastructure rather than corporate optimization, Breitenbach makes the case that the next frontier of artificial intelligence may not just improve efficiency, it could help eradicate hunger itself.
1Q: The U.S. produces $382 billion in surplus food every year, yet 47.9 million Americans are food insecure. How do you make sense of that paradox?
There’s really no clean way to make sense of it. It’s a contradiction we’ve all gotten used to for decades, but it shouldn’t exist. With r4’s predictive AI and Smart Food Program, we believe we can help fix this problem.
At the heart of it, we don’t have a supply problem. We produce more than enough food. What we have is a coordination problem. Where the food is and where the demand is aren’t connected in any real-time, system-level way.
So, you end up with inefficiency at scale: surplus on one side, unmet demand on the other.
That’s the gap, and it’s exactly what we’re solving at r4.
2Q: The food industry has invested heavily in AI for predictive analytics, cold-chain optimization, and demand forecasting. So why hasn’t that investment closed the gap?
Almost every AI tool deployed in food supply chains is designed to optimize one enterprise at a time. Yet the inefficiencies driving waste don’t live inside companies — they live between them.
A retailer’s overstock doesn’t communicate with a distributor’s routing algorithm, and a food bank’s real-time demand signal never reaches the manufacturer holding surplus inventory two states away.
Every node in the chain is getting smarter in isolation, while the system continues hemorrhaging food. Better algorithms inside silos are still silos.
3Q: You’re describing a coordination failure. Why has that been so hard to fix?
It’s been hard to fix because no one has really owned the space between organizations — the holistic, predictive view across the entire ecosystem.
There are real concerns around sharing data, especially in competitive environments. On top of that, commercial food systems and food relief networks evolved separately, with different priorities and different infrastructure.
The bridge never got built. Not because the need wasn’t obvious, but because no one was truly positioned to connect both sides.
That’s the gap we’re focused on closing with r4 XEM.
4Q: Tell us about r4’s approach. What does cross-enterprise AI mean in practice?
r4’s XEM AI software connects data and decision-making across organizational boundaries throughout the ecosystem of manufacturers, distributors, retailers, and food relief organizations.
When those networks share real-time signals, commercial surplus can be dynamically matched to community need as a designed outcome, not an afterthought.
We recently put that into practice by coordinating the distribution of 30 tons of surplus food across eight states, moving perishable inventory to food relief organizations before it expired.
Fewer trucks run half-empty, less food goes to landfill, and more food reaches people who need it.
5Q: What does “dynamically matched in real time” look like on the ground?
Today, when a regional distributor ends up with excess perishable inventory, by the time that information reaches a food relief organization through manual processes, the window to act has often already closed.
We enable a predictive system where that excess inventory signal becomes instantly visible to the right partners, while the logistics required to move it are already being optimized in the background.
Our 30-ton, eight-state distribution effort is a real proof point. That volume moved because the coordination happened faster than any manual process could support.
That’s the difference between AI that helps a company manage its own operations and AI that changes what the entire system can do.
6Q: There’s a growing debate about whether AI in food systems is being used for people experiencing food insecurity or to them. Where does r4 fit in that argument?
We believe in AI for good. This new era of AI is going to fundamentally change the world we live in for the better.
With the r4 Smart Food Program, the focus is simple: increase the flow of resources to communities that need them.
Connecting surplus to demand isn’t about monitoring people. It’s about building infrastructure that helps the system work better for everyone.
7Q: The 2025 federal budget law authorized over $1 billion for AI projects. Where should that investment go if the goal is reducing food insecurity?
Those investments should go toward building new public-private capabilities that make producers more profitable while also helping eliminate hunger.
That means investing in transformation — connecting commercial systems with agencies that have traditionally operated separately, such as Health and Human Services and the USDA.
The opportunity now is to create infrastructure where public and private organizations can coordinate in entirely new ways.
8Q: Critics argue that AI in food systems primarily benefits large corporations and leaves food-insecure communities behind. How do you respond?
Commercial enterprises will always have a profit motive. That’s the reality of the system.
What’s exciting is that advanced AI technologies now make it possible to align those incentives with public outcomes through new public-private partnerships.
Government has an imperative to improve access and outcomes for citizens. Cross-enterprise AI creates an opportunity for commercial and public organizations to work together in ways that better serve communities while also improving efficiency across the system.
9Q: What would you say to a food bank skeptical of AI that sees it as a tool built by and for the commercial food industry?
I’d say that skepticism makes sense.
At the same time, this is the moment to lock arms and connect the excess capacity that already exists across the system so we can work together toward the shared mission of eliminating hunger.
We’ve already seen what’s possible when those connections exist, and we’re honored to help build capabilities that support food banks and the people working every day to solve this problem.
10Q: What does success look like for the food system five years from now?
That’s such an exciting question.
Imagine a food system that is more profitable, where food prices actually go down, hunger becomes a thing of the past, and people are healthier because nutritious food is more affordable and accessible.
Success looks like a system where surplus is the anomaly rather than the structural norm, and where the intelligence layer is sophisticated enough to reroute overproduction in real time instead of discarding it.
It looks like food banks in high-need communities having access to the same level of forecasting, logistics, and decision support as a Fortune 500 retailer.
