Artificial Enthusiasm: Why an AI-Built Store in Two Evenings Is Just the Beginning

⚡ The gist in 5 seconds - The point: a column on Habr about how a person built a working cat food store with AI in just two evenings — but what happens next is unknown. - Where to find it: published on Habr on September 27, a 7-minute read, with 13K views. - Limitation: the author doesn't offer ready-made solutions, but urges readers to figure out for themselves where AI can be given the work, where every step needs checking, and where it's cheaper to do it yourself. ### 🔍 What was found A column by Columnistdc titled "More Thoughts on Universal Artificial Enthusiasm" was published on Habr on September 27 at 23:28. It's an opinion piece, not a how-to: the author asks readers to imagine a typical situation — yet another person, in yet another two evenings, has used AI to build a cat food store, and it works. The person is happy, "a new era of development has arrived." But then the author suggests watching how this system behaves at the first change: for example, when adding a discount makes pickup delivery disappear. And it would be great if, by that point, the author of the story about saving on programmers hadn't finished yet. Throughout the text, the gap between the speed of writing code and the real complexity of development is repeatedly emphasized. It's noted that behind the demand to "speed up the team's work" there is often a desire to somehow plan the work at all: the promised four weeks turn into eight, and everyone finds out on the last day of the fourth. There's also an observation about an AI assistant that, for the third time, is fixing the consequences of its previous fix — that's also the result of an experiment, even if it doesn't look as impressive against the backdrop of tales of magical success. It's separately noted that selling the competency of Chief AI Transformation & Visionary Strategist is harder when your conclusion is "it's not needed here yet." ### 💡 Why it matters The column is valuable because it shifts the focus from enthusiasm about AI's capabilities to the pragmatic question of the limits of its application. Instead of promising that programmers will no longer be needed, the author suggests learning from one's own experiments where work can be handed to an assistant, where every step will have to be checked, and where it's cheaper and calmer to do it yourself. The key is to notice in time when the chosen approach stops paying off and to communicate this to management, rather than simply speeding up code writing when the bottleneck lies in approvals and planning. ### 🧩 Context The author recalls that before AI, speed problems were traditionally "treated" with crunches and evening work, but now there's a participant who doesn't need weekends, creating a temptation not to change processes. Meanwhile, business consultants and strategists know how to translate the significance of AI into money and paint pictures of total adoption, while developers are often in confusion: some ignore the topic, others act like Luddites, and still others demand using AI for every little thing. The author urges readers to look around and see an "untapped field" of opportunity: things that were constantly neglected due to a lack of hands, and to communicate to management that before

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