---
title: "our workplace LLM mass delusion"
url: https://daily.dev/posts/our-workplace-llm-mass-delusion-4krxasrbu
source_url: https://blog.avas.space/llm-circus
type: article
source: "Lobsters"
published: 2026-06-11T18:26:31.321Z
updated: 2026-06-11T18:26:55.126Z
tags: ["ai", "github", "llm", "chatgpt"]
reading_time: 9
upvotes: 71
comments: 7
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# our workplace LLM mass delusion

**[Lobsters](https://daily.dev/sources/lobsters)** · 9 min read · 71 upvotes · 7 comments

## Summary

A frustrated employee shares their firsthand experience of what they describe as irrational AI hype in their workplace. Despite severe budget constraints — cancelled bonuses, dropped licenses, understaffed departments — the organization spent heavily on AI consultants, LLM workshops, ChatGPT and Copilot licenses. Recurring company-wide demos produced zero successful use cases across all teams. Presented use cases included asking ChatGPT how it 'feels,' using it to read a one-page lunch menu, and uploading suspicious emails to it. The author argues this reflects a mass delusion amplified by the Dunning-Kruger effect, where leadership chases AI hype while real operational needs go unmet, and that the speed of AI adoption exposes how organizational inertia is a choice, not an inevitability.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.avas.space/llm-circus>

## Community discussion

Top comments from developers on daily.dev.

**@mrtbg22** · 13 upvotes

> I genuinely hope we're arriving at the point where the bubble is going to burst soon. There are definitely good use cases and smart ways to integrate AI assisted development but delegating entire implementations, giving full code access and letting it rip never seemed like my kind of thing. It felt like I lose control over my own codebase and the work that it initially seemed to "save" me was just waiting around the corner after a few new feature requests to bite me even harder. I'd rather spend more time over something I have to produce but have all the confidence later down the line that if...

**@elmarchavez** · 2 upvotes

> The article just shows how out of touch non-technical (and even some technical) leads are. AI is good if implemented accordingly with a solution that solves a recurring problem. It is not if you just sprinkle AI just to be called an AI company and call it "progress".
>
> ![stonks.png](https://media.daily.dev/image/upload/s--l1hhEy-F--/f_auto/v1781272531/ugc/content_43860e3b-7173-4ebf-9578-c5dd3358298f?_a=BAMAMiWQ0)

**@kunozoltner** · 0 upvotes

> If the AI would at least have analyzed the cafeteria excel sheet to detect possible allergies or similar things - that would have been at least somewhat useful 🤓

**@ajaychoudhary80** · 0 upvotes

> The problem isn't AI—it's deploying it without a clear business case.

**@joncoe** · 0 upvotes

> So many 'decision makers' will have egg on their face from all this. The AI consultant at my workplace is insufferable. "AI for everything.. look at this summary it made..." where it takes but moments to just skim and cull what you need from the Jira docs. What a waste of time.

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---

Tags: [#ai](https://daily.dev/tags/ai), [#github](https://daily.dev/tags/github), [#llm](https://daily.dev/tags/llm), [#chatgpt](https://daily.dev/tags/chatgpt)

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