Where AI Applications Will Actually Win: An Investment Thesis

2026-03-22 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Robotics & Autonomous Systems · Depth: Advanced, long

Summary

The article presents an investment thesis arguing that the next wave of AI applications will achieve significant wins by targeting "field-based work" rather than the "screen-based work" that has historically dominated software investment. While 80% of the global workforce performs field-based tasks, less than 1% of enterprise software has been built for them. This gap exists because traditional software interfaces assumed a keyboard and screen, which are incompatible with field environments. Five key AI advancements—voice and camera as primary interfaces, near-zero cost of complex thought, organizational memory at scale, goal-oriented agents, and zero marginal cost of creation—have removed these constraints. The author identifies seven business mechanics, including charging per thought and owning the function, that determine the durability and profitability of AI businesses in these emerging markets. Eleven specific startup opportunities are highlighted across health, field service, retail, restaurant operations, creative production, executive assistance, SMB manufacturing, compliance, and education.

Key takeaway

For investors and entrepreneurs seeking high-growth opportunities, focus on AI applications for field-based work. Your strategy should prioritize building products with voice and camera interfaces, owning entire job functions, and integrating institutional memory to create strong switching costs. Additionally, adopt usage-based pricing models to ensure profitability as AI inference costs fluctuate, and consider how zero marginal cost outputs can restructure market competition.

Key insights

AI advancements enable software to finally address the vast, underserved market of field-based work.

Principles

• Interface design dictates market reach.
• Own the function, not the feature.
• Institutional memory creates switching costs.

Method

Identify large, undercrowded markets where AI's new capabilities (voice/camera, low-cost reasoning, large context, agents, zero marginal cost) remove prior software constraints and apply specific business mechanics for durable growth.

In practice

• Prioritize voice/camera interfaces for field workers.
• Implement usage-based pricing for AI services.
• Build products that accumulate organizational memory.

Topics

• Field-Based AI Applications
• Multimodal AI Interfaces
• AI Agents
• AI Business Models
• Organizational Memory

Best for: Executive, Product Manager, Investor, Entrepreneur, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.