Why Abundant Intelligence Won’t Solve Our Biggest Problems

2026-05-03 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Fundamental Awareness, quick

Summary

The article introduces the "Shelter Benchmark" as a new standard to evaluate the real-world impact of artificial intelligence, specifically its ability to address foundational societal problems like the housing crisis. It argues that despite AI being heralded as a universal solution for tasks such as protein design, code generation, and physics research, it performs poorly when measured against its capacity to solve tangible physical needs like affordable housing. The author contends that AI's failure in this area necessitates a deeper examination of how intelligence is defined, the dynamics of power, and whether current technological efforts are misdirected towards solving less critical problems.

Key takeaway

For policymakers and technology strategists evaluating AI's societal contributions, you should apply the "Shelter Benchmark" to assess its tangible impact on critical physical needs like housing. This framework helps identify whether AI development is genuinely addressing foundational problems or merely optimizing less impactful areas, guiding resource allocation towards solutions with direct societal benefit.

Key insights

AI's true impact should be measured by its ability to solve fundamental physical needs, not just abstract problems.

Principles

• New technologies require real-world impact benchmarks.
• Societal problems are often interconnected.

Topics

• Artificial Intelligence
• Shelter Benchmark
• Housing Crisis
• Societal Problems
• Technology Impact

Best for: AI Ethicist, Policy Maker, Executive

Related on AIssential

• AI benchmarks are broken. Here’s what we need instead. — Current AI benchmarks misrepresent real-world performance by ignoring human teams and organizational context.
• A Theoretical Framework for Adaptive Utility-Weighted Benchmarking — Benchmarking AI systems should be a dynamic, human-aligned process, not a static technical exercise.
• The Hammer of AI: When Every Problem Looks Like a Nail — Applying AI universally, regardless of problem type, leads to significant societal and individual costs.
• The Download: AI’s impact on jobs, and data centres in space — Understanding AI's societal and environmental impacts requires specific economic data and innovative infrastructure solutions.
• Four Experts on the Questions We Should Be Asking About AI Right Now — AI's evolving impact necessitates proactive, cross-sectoral collaboration to address long-term societal changes, equitable benefits, knowledge integrity, and information overload.
• Our Collective Bike Shed Moment — Humanity is trivializing the profound implications of advanced AI, focusing on minor issues due to normalcy bias and the "Somebody Else's Problem" field.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.