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Information Farming: From Berry Picking to Berry Growing

Leif Azzopardi, Adam Roegiest

TL;DR

This paper argues that Generative AI reframes information seeking from patch-based foraging to cultivation-driven Information Farming, drawing a parallel with the Neolithic shift from hunting to farming. It introduces a framework where users plant seed prompts, cultivate workflows with AI agents, and harvest structured, domain-relevant outputs within their own plots, aiming for higher yield, reproducibility, and control. By revisiting Berry Picking and Information Foraging Theory, the authors analyze how GenAI alters costs and decisions, and they discuss design, evaluation, and governance implications along with risks such as cognitive debt and misinformation. The practice-oriented discussion outlines seeding, growing, pruning, fertilizing, re-planting, harvesting, and packaging as core activities, arguing for new metrics and infrastructural support to enable scalable, responsible information farming.

Abstract

The classic paradigms of Berry Picking and Information Foraging Theory have framed users as gatherers, opportunistically searching across distributed sources to satisfy evolving information needs. However, the rise of GenAI is driving a fundamental transformation in how people produce, structure, and reuse information - one that these paradigms no longer fully capture. This transformation is analogous to the Neolithic Revolution, when societies shifted from hunting and gathering to cultivation. Generative technologies empower users to "farm" information by planting seeds in the form of prompts, cultivating workflows over time, and harvesting richly structured, relevant yields within their own plots, rather than foraging across others people's patches. In this perspectives paper, we introduce the notion of Information Farming as a conceptual framework and argue that it represents a natural evolution in how people engage with information. Drawing on historical analogy and empirical evidence, we examine the benefits and opportunities of information farming, its implications for design and evaluation, and the accompanying risks posed by this transition. We hypothesize that as GenAI technologies proliferate, cultivating information will increasingly supplant transient, patch-based foraging as a dominant mode of engagement, marking a broader shift in human-information interaction and its study.

Information Farming: From Berry Picking to Berry Growing

TL;DR

This paper argues that Generative AI reframes information seeking from patch-based foraging to cultivation-driven Information Farming, drawing a parallel with the Neolithic shift from hunting to farming. It introduces a framework where users plant seed prompts, cultivate workflows with AI agents, and harvest structured, domain-relevant outputs within their own plots, aiming for higher yield, reproducibility, and control. By revisiting Berry Picking and Information Foraging Theory, the authors analyze how GenAI alters costs and decisions, and they discuss design, evaluation, and governance implications along with risks such as cognitive debt and misinformation. The practice-oriented discussion outlines seeding, growing, pruning, fertilizing, re-planting, harvesting, and packaging as core activities, arguing for new metrics and infrastructural support to enable scalable, responsible information farming.

Abstract

The classic paradigms of Berry Picking and Information Foraging Theory have framed users as gatherers, opportunistically searching across distributed sources to satisfy evolving information needs. However, the rise of GenAI is driving a fundamental transformation in how people produce, structure, and reuse information - one that these paradigms no longer fully capture. This transformation is analogous to the Neolithic Revolution, when societies shifted from hunting and gathering to cultivation. Generative technologies empower users to "farm" information by planting seeds in the form of prompts, cultivating workflows over time, and harvesting richly structured, relevant yields within their own plots, rather than foraging across others people's patches. In this perspectives paper, we introduce the notion of Information Farming as a conceptual framework and argue that it represents a natural evolution in how people engage with information. Drawing on historical analogy and empirical evidence, we examine the benefits and opportunities of information farming, its implications for design and evaluation, and the accompanying risks posed by this transition. We hypothesize that as GenAI technologies proliferate, cultivating information will increasingly supplant transient, patch-based foraging as a dominant mode of engagement, marking a broader shift in human-information interaction and its study.
Paper Structure (10 sections, 2 figures, 2 tables)

This paper contains 10 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Berry Picking
  4. Information Foraging Theory
  5. From Foraging to Farming
  6. Information Farming
  7. Foraging vs. Farming
  8. Information Farming in Practice
  9. Discussion
  10. Summary and Conclusion

Figures (2)

  • Figure 1: The Berry Picking Model where the path users take through the information space evolves as they encounter new information (given their queries Q and their thoughts T collecting berries along the way until the end -- the basket of berries).
  • Figure 2: The Berry Growing Model where the user plants an initial seed prompt (P0) and then cultivates their crop through iterations: growing (P1), removing pests (P2), weeding (P3), and other activities until harvest (P4).