The bonfire of our vanities
Summary
The article "The bonfire of our vanities" offers a critical introspection into the experience of developing with large language models, questioning the true nature of "work" and "building" in this new era. It describes an initial burst of productivity where LLMs rapidly generate code, quickly followed by a descent into confusion, exceptions, and unconsidered complications. The author likens the resulting applications to a "blurry JPEG" of the original vision, characterized by endless iterations, tech debt, and a constant need for new prompts, resembling a "slot machine" addiction. The piece also explores the evolving definition of work, from stable jobs to the "Entrepreneur" leading an "army of agents," often with zero employees. Ultimately, it suggests this "mania for building" with AI might be a transient "epidemic" driven by vanity, predicting that the novelty will fade, subsidies will disappear, and many will eventually question the true destination of this "delirious moment."
Key takeaway
For entrepreneurs or AI/ML directors considering extensive LLM-driven development, recognize that the initial velocity often yields diminishing returns and significant tech debt. You should critically evaluate whether your "building" truly addresses a market need or if it's an addictive cycle of iterative prompting. Prioritize clear product vision and user validation over endless AI-generated feature additions, as the current "mania" may be a transient phase, risking wasted resources on projects that ultimately lack substance or adoption.
Key insights
The rapid, iterative development enabled by AI often leads to chaotic, unfulfilling "building" driven by vanity, whose long-term value is questionable.
Principles
- AI-driven development often creates "blurry JPEGs" of original visions.
- Constant prompting can lead to an addictive "slot machine" development cycle.
- The current AI "building" boom may be a transient "epidemic" of vanity.
Method
The article describes an iterative process: prompt, generate, encounter exceptions, plan new phases, repeat, often leading to multiple, unintegrated versions.
In practice
- Rebuilding with "better models" (Opus 4.8, GPT 5.6) is a common impulse.
- Combining multiple AI-generated app versions is a frequent task.
- Continuously refining UI elements (e.g., "too far to the left") is typical.
Topics
- Large Language Models
- AI Development
- Prompt Engineering
- Tech Debt
- Product Vision
- Entrepreneurship
Best for: Director of AI/ML, Entrepreneur, Tech Journalist
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Editorial summary, takeaway, and curation by AIssential. Original article published by benn.substack - Benn.substack.com.