I have officially stalled.

2026-07-25 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, short

Summary

The article describes the author's feeling of "stalling" in the AI space, drawing a parallel to the "stall" phenomenon in BBQ. After an initial period of rapid advancement and adoption of AI tools like Claude, MCPs, and agent-led development, the author observes a slowdown in novel capabilities and a shift towards "business-as-usual." This stagnation is marked by repetitive AI-generated content, "loop engineering," and companies re-evaluating eye-watering spending on tokens, exploring open-source options, LLM gateways, and even penalizing high AI usage. The author feels a mental disconnect, constantly validating AI output and missing the human voice in writing, leading to a reduced inclination for "All-AI-all-the-time" approaches.

Key takeaway

For Directors of AI/ML evaluating current spending and future strategy, recognize that the initial rapid AI adoption phase is maturing. You should scrutinize token costs and explore open-source or LLM gateway solutions to optimize expenditure. Be mindful of "AI fatigue" and the need to balance AI integration with preserving authentic human input, especially in critical documentation. This period demands patience and strategic re-evaluation, not just scaling up.

Key insights

The rapid initial phase of AI innovation is giving way to a "stall" period, characterized by diminishing novelty and increased cost scrutiny.

Principles

• Innovation cycles include periods of perceived stagnation.
• Over-reliance on AI can dilute authentic human expression.
• Cost pressures drive exploration of open-source LLMs.

In practice

• Explore open-source self-hosted LLMs.
• Implement LLM gateways for model selection.
• Validate AI-generated content for authenticity.

Topics

• AI Adoption Trends
• Large Language Model Costs
• Open-Source LLMs
• AI Agent Orchestration
• Innovation Stagnation
• AI Content Authenticity
• LLM Gateways

Best for: CTO, VP of Engineering/Data, AI Architect, Director of AI/ML, Consultant, Tech Journalist

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