Using focused AI to help small farmers and reduce food insecurity

What if narrow #AI, rather than imagined AGI through scaling will be what changes the world? In some places, that's already happening. El Mahdi Aboulmanadel founded DeepLeaf [https://deepleaf.io/] after watching smallholder farmers in Morocco misdiagnose crop disease because three distinct conditions can look identical to the human eye. Wrong diagnosis, wrong treatment, chemical residue on food. Best case scenario? Export crops rejected at customs. Worst case scenario? Food scarcity for communities that can't afford it. DeepLeaf's answer is deliberate focus: one problem, field-validated data, models trained on hyperspectral and RGB image pairs across 57 crops. The accuracy doesn't come from scale. It comes from specificity. Fine-tuned continuously on new field data. The result is less compute, faster iteration, and outcomes closer to the ground truth. DeepLeaf has both cloud inference for large or multi-crop operations and lightweight edge models downloaded per crop for farmers running on Android phones in areas with no connectivity. The architecture fits the user, not the other way around. We get into economic potential for farmers, and of course, the effects of the war in Iran. This episode is about what new AI perspectives than the ones taking up all the oxygen in the West. This is technology that's built for communities that Silicon Valley usually ignores.