What AI Is For (www.chrbutler.com)

After three years building with AI, the author argues it’s a useful technology that’s nevertheless wildly overhyped—best case a classic bubble, worst case deliberate overstatement by those profiting most. In hands-on design work generative models often under-deliver: prompt engineering is time-consuming, models struggle to reproduce bespoke illustrative styles, text-image relationships, and production-ready layouts, and outputs often require rebuilding in tools like Figma. The pattern repeats across enterprises: small, focused applications (information synthesis—search, summarization, analysis) show clear ROI, while attempts to automate end-to-end workflows incur equal or greater costs and heavy maintenance. The author, cofounder of AI-driven Magnolia, emphasizes the enormous investment needed to sustain quality that competes with general-purpose models and cites corporate studies showing isolated targets succeed where blanket AI strategies fail. Beyond technical limits, the piece flags systemic risks: speculative valuations tie the largest firms together, creating a fragile market prone to a severe correction; the AGI narrative is vague and unmeasurable; and generative AI magnifies misinformation. The author also posits a political-economic angle: AI hype legitimizes massive datacenter builds that consume land, water, and energy—assets that may outlast the tech’s value and concentrate power in quasi-sovereign private estates. The takeaway: prioritize narrow, verifiable use cases and scrutinize the incentives driving large-scale AI expansion.