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Marvin Okafor is not leaving software to become a farmer. He is still working as a software engineer, using AI every day, and researching a farm he does not yet have. His essay, “I Use AI Every Day. I’m Also Googling Crop Rotation.”, is about holding those interests together: learning to work with AI while exploring a more physical, food-focused venture.
Why AI has him thinking beyond software
Okafor describes more than eight years in software and roughly a year and a half of daily AI use. He says AI has made him faster and taken on some coding work he once considered central to his identity. That has not erased the other parts of engineering: judgment, context, and responsibility still matter.
His questions are personal, not a forecast about everyone’s career. He asks, “how do I stay employable?” and “what can I build that still needs a human when the code is cheap?” In response, he is exploring an additional direction rather than announcing a departure from tech.
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What the farm idea means—and what it does not
The farm is an idea he is researching, not an operating business. Okafor explicitly says he does not have it yet. He mentions land, crops, fish, cocoa, and food production as possibilities, while describing the practical learning that comes with the idea: soil, seasons, and crop rotation.
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“People have to eat,” he writes, capturing the appeal of work tied to tangible human needs. That is a reflection on why food production interests him, not evidence that farming is insulated from automation or a recommendation that software workers should change careers.
Why crop rotation is part of the learning curve
Crop rotation means changing which crops grow in a given area over successive growing periods. The University of Minnesota Extension’s garden guidance, reviewed in 2024, explains that gardeners generally plan rotations around plant families: related plants can share pests and diseases, so moving families can help interrupt those problems.
Rotation can also help reduce nutrient depletion and make use of crops’ different rooting patterns. Legumes such as beans and peas can add nitrogen through their relationship with rhizobia. These are general principles, not a ready-made plan for Okafor—or for any farm without details about its crops, soil, location, and growing system.
What a real rotation plan has to account for
For a practical plan, crop family is only one consideration. Minnesota Extension’s farm guidance also points to disease, insect and weed pressure; soil fertility and structure; moisture; economics; and the timing of work. A sequence that makes sense for a home garden may not fit a commercial operation, and recommendations from Minnesota may not suit another climate.
Anyone moving from curiosity to a planting plan should consult an agricultural extension service or other locally relevant source. The right rotation depends on local conditions and the specific crops involved.
AI can enter agriculture without replacing its experts
AI is not limited to software development in Okafor’s imagining of the future. A company feature from Microsoft Signal describes the Land O’Lakes Oz copilot asking farmers for context such as location, crop rotation, nutrient levels, and growing conditions before offering guidance. The feature also emphasizes accuracy and knowledgeable human involvement.
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That example illustrates one possible use of AI as decision support: gathering relevant information and helping people work through it. It is a company-reported feature, not an independent performance trial, and it does not establish that AI replaces agronomists or farmers’ judgment.
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Okafor’s essay is compelling because it does not pretend to resolve uncertainty. He is grateful for the speed AI brings to his work, uneasy about what that change means for his identity, and curious about building something connected to land and food. Googling crop rotation is a small, concrete step in that exploration—not proof of a career pivot.
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