Zero Hunger vs Zero Anger: Rethinking AI for Food Justice

Yes, zero hunger is a goal. But if we’re honest, it’s only half the story. We need to ask: what about the anger? The frustration that comes when people lose not just food but the ability to live as they always have. The goal isn’t to replace agronomists or elders. It’s to make their knowledge visible. Because what often drives food insecurity is not just lack of resources—it’s disempowerment.

By Florence Kim

Visual concept by Florence Kim and generated by MidJourney

I’ve seen people walk for days with nothing but hope and a plastic jerrycan in Djibouti. In Kenya, I’ve seen livestock (once a family’s wealth and memory) left behind to die when there was no pasture left. I’ve tasted sugar soup in camps where there was nothing else to distribute in Central African Republic. And I’ve seen what happens when one community gets help and the next one doesn’t. It breeds resentment, not relief.

So yes, zero hunger is a goal. But if we’re honest, it’s only half the story. We need to ask: what about the anger? The frustration that comes when people lose not just food but the ability to live as they always have. When farmers become migrants. When culture is abandoned out of necessity. When hunger makes you invisible.

AI, as it’s used now, mostly helps with speed: forecasting where the next crisis will hit, helping decision-makers react faster. But what if it could do more than react? What if AI could help communities imagine and build food systems that are fairer, more sustainable, and actually worth staying for? What if AI wasn’t just used to optimize current food systems but to design entirely new food ecologies? Not industrial, extractive models built far from the communities they affect, but community-led regenerative systems, built on a mix of local wisdom and powerful technology. Systems that respect ecosystems, reduce dependency on external aid, and offer real ownership to the people who rely on them.

AI could help design these systems through simulation, co-creation, and analysis. Imagine a tool that allows farmers to test different scenarios before planting a single seed. Could they grow millet instead of maize this year? What happens to yield, soil health, and water use if they do? What about mixing crops, or switching from irrigation to dryland techniques? Could they revive an ancestral planting pattern and still meet nutritional needs?

We could train AI models not only on rainfall data or soil diagnostics, but on oral histories, seasonal rhythms, and intergenerational practices. AI could help simulate climate-resilient micro-ecosystems, suggesting polycultures instead of monocultures, encouraging seed sovereignty, and helping communities rewild degraded land while still feeding themselves.

Think beyond apps or dashboards. Imagine 3D visualization tools that allow community leaders to walk through a digital model of their village, testing where to build small dams or plant trees for shade and moisture retention. AI-assisted land restoration plans could integrate biodiversity preservation and carbon capture with food security goals. Farmers could print personalized seed guides or access local crop disease predictions through a voice assistant in their own language, even offline.

The goal isn’t to replace agronomists or elders. It’s to make their knowledge visible, validated, and powerful in decision-making. Because what often drives food insecurity is not just lack of resources—it’s disempowerment. And this matters. Because when people are forced to abandon farming, they often abandon more than land. I’ve seen it: pastoralists who lose their herds due to drought may never return to that life. They move to cities, take up informal work, and lose the rituals and rhythms that shaped who they are. Hunger, in that sense, becomes a form of cultural erasure. It rewrites identities.

That’s why regenerative, locally-rooted, AI-supported food systems matter. They offer an alternative path, not just survival, but sovereignty.

But to get there, we need to stop treating AI like a logistical trick to deliver calories faster. We need to treat it as a co-designer of futures. And that means making AI tools accessible, open-source, culturally literate, and co-created with the communities they aim to serve.

AI should serve the people most affected by the crises it’s meant to address. That means shifting the focus from speed to sovereignty, from efficiency to equity. AI tools should not just react to hunger but help prevent it by supporting food systems that are just, regenerative, and rooted in local knowledge.

It’s not about replacing the farmer. It’s about respecting the farmer and giving them back the power to choose, adapt, and thrive.

Because real food security isn’t just about zero hunger. It’s also about zero anger.