How chatGPT-Taught Experts Are Crippling Agentic AI

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How chatGPT-Taught Experts Are Crippling Agentic AI<br>Watching Slides Will Not Result In Understanding Agents

Maria Sukhareva<br>Aug 20, 2026

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For decades AI was an obscure field covering Natural Language Processing, Computer Vision, Bioinformatics and similar areas. People considered it a difficult research area with unclear value. When deep learning gained momentum, the field got even more complex - research papers filled with mathematical formulas, code that no one knows how to run, and constant CUDA errors when you try to train something.

The output, though, was fairly understandable - we can classify reviews on Amazon for your product into good or bad, divide your documents into invoices and contracts, find all the addresses and people’s names, translate documents. Those were obvious repetitive tasks, not hard to understand in terms of what they could do and very hard to understand in terms of how. But the capabilities were clear and they fitted neatly into existing workflows and established processes. And when the what is that clear, working out the how is a technicality.<br>And then something interesting happened - ChatGPT arrived.<br>I thought this made things worse - now it was hard to understand what it can do and hard to understand how.<br>Not everyone shared my opinion. A colleague, in a heated argument with me about the need for deep learning experts as such, said something like: “AI is very simple now, anyone can do it, you do not need AI experts anymore, anyone can do AI.”<br>What made him think this was the gap between his own lack of expertise in AI and the convincing eloquence of GPT-3.5 - the first model of its kind to reach a large audience, and the one that spread hallucination and sycophancy along with it. Very quickly that model convinced him that he had a deep and profound understanding of how AI works and what it can do. And so it happened with many others. I call them chatGPT-taught experts.

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The plague of advisory slop

I wish I had known the word slop back then, because I was lacking a concept to describe those presentations - gorgeous in aesthetics, horrendous in content - produced by fairly expensive and well known consulting agencies that target C-level managers and decision makers.<br>Surprisingly to me, managers would far rather watch this slop for hours, sit through executive briefings and AI strategy workshops, again filled with slop presentations, than spend one day trying AI output themselves.<br>And that is how we ended up with atrocities like YourCompanyAI - a useless in-house model, built in the spirit of BloombergGPT, which was purpose-built from scratch on forty years of financial data and then beaten by general-purpose GPT-4 on almost every financial task within weeks of its release. Or a RAG bot that desperately hallucinates about the documents it found on the company intranet. Or a customer support assistant that customers avoid like the plague.<br>Most of the workforce was experiencing a kind of cognitive dissonance. On one side, the news and the big minds of AI were talking about the impending white collar bloodbath, and companies were boasting about AI related layoffs. On the other, all the employees actually had was Microsoft Copilot and a multitude of DIY chatbots that were of absolutely no use to them.<br>So how did it happen that a technology that has become genuinely good and advanced over the last four years has had so little impact on the workforce, and even worse - many of those whose productivity it was supposed to boost do not want to use it, and consider it dumb, hallucinating and dangerous?<br>That is where I come to the key argument of this article. You cannot understand what large language models can do, particularly in combination with their agentic functions, unless you try them. And, thus, you cannot take an informed decision about anything related to agentic AI.

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