How We Bet Against the Bitter Lesson

2026-03-02 · Source: AI & ML – Radar · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Emerging Technologies & Innovation · Depth: Advanced, long

Summary

The article introduces "Agent Skills" and similar concepts like Jesse Vincent's Superpowers and Anthropic's Plugins for Claude Cowork, proposing them as foundational elements for a new AI-human knowledge economy. These Skills combine natural language context (for probabilistic LLMs) with deterministic tool calls (for traditional code), enabling AI agents to perform complex tasks like diagnosing production incidents or conducting financial analysis by integrating expert judgment with executable actions. This approach aims to save computational tokens by offloading deterministic tasks to tools, making LLM interactions more efficient. The author argues that AI, in its current form, functions as a social and cultural technology that extends human knowledge rather than replacing it, emphasizing the need for mechanisms to incentivize human knowledge creation and sharing. The piece also explores challenges such as intellectual property protection, skill discovery, evaluation, and the economic plumbing required for compensating human experts.

Key takeaway

For AI Architects and CTOs evaluating agentic AI solutions, recognize that "Skills" represent a critical interface for integrating human expertise with AI capabilities. Prioritize solutions that enable the packaging of tacit knowledge and deterministic tools, and advocate for economic models that compensate human experts for their contributions. Your focus should be on building infrastructure that supports composability, robust security, and effective evaluation of these hybrid AI-human workflows to ensure long-term value creation.

Key insights

Agent Skills integrate human expert judgment and deterministic tools to create a new, efficient AI-human knowledge economy.

Principles

• AI extends human knowledge, not replaces it.
• Value migrates to adjacent layers when a product commoditizes.
• Well-designed Skills save tokens by offloading deterministic tasks.

Method

Agent Skills combine natural language context (Markdown instructions, domain knowledge, examples) for probabilistic LLMs with deterministic tool calls (code) for tasks LLMs struggle with, enabling integrated expert workflows.

In practice

• Package architecture overviews and runbooks as Skill context.
• Use tools for querying monitoring systems and running diagnostic scripts.
• Design Skills to be selective with context to save tokens.

Topics

• Agent Skills
• Large Language Models
• Tacit Knowledge Encoding
• AI Agent Orchestration
• AI Economy

Code references

• obra/superpowers
• anthropics/knowledge-work-plugins
• modelcontextprotocol/modelcontextprotocol
• Agent-Card/ai-card
• oreillymedia/skills-and-the-future-knowledge-economy

Best for: AI Architect, Entrepreneur, CTO, AI Engineer, MLOps Engineer, AI Product Manager

Related on AIssential

• How to Use Agent Skills — Agent skills enable reusable, dynamic AI capabilities, moving beyond static prompts for improved performance and reliability.
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• Learn the system — Over-reliance on AI coding agents risks developer skill atrophy and cognitive debt, despite productivity gains.
• Packaging Expertise: How Claude Skills Turn Judgment into Artifacts — Claude Skills package organizational expertise into versionable, governable artifacts, enabling scalable AI agent training.
• Soft Forks: How Agent Skills Create Specialized AI Without Training — Agent Skills enable specialized AI behavior at runtime through context injection, offering a cost-effective alternative to fine-tuning.
• Agent Building Trends [Operator Bonus Episode] — AI agent development is rapidly evolving towards autonomous digital employees and highly personalized "markets of one" software.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI & ML – Radar.