Vibe-Coding: The AI Agent-Illusion

The current generation of AI models, especially large language models (LLMs), is based on architectures that are primarily designed to recognize patterns in huge amounts of data and generate statistically probable word sequences. This leads to a kind of "semantic fuzziness": the AI doesn't really "understand" the meaning behind the words like a human does, but mimics human speech extremely convincingly. For us humans, the results often seem surprisingly plausible, coherent and even creative. However, it is precisely this plausibility that leads us to be amazed again and again when it turns out that the generated information, despite its convincing form, is factually false or nonsensical – a direct consequence of this architecturally induced fuzziness in "understanding".

Semantic fuzziness in practice: When AI search engines are 60% wrong

How relevant this problem of semantic fuzziness is in practice is underlined by a recent article by Ars Technica. It reports on a study (AI Search Has A Citation Problem) that comes to the alarming conclusion that AI-powered search engines, such as Perplexity, provide incorrect or misleading answers in around 60% of cases. This impressively shows how the ability of AI to generate plausible but not necessarily correct information becomes a real challenge in the search for information and underlines the need for critical examination of the results.

When AI flow becomes a trap

The term "vibe coding" is doing the rounds and describes what many developers now do on a daily basis: programming with the help of large language models (LLMs) such as ChatGPT, Claude and Co. You specify a requirement, let the AI generate code, maybe adjust it a little – done. At first glance, this sounds temptingly fast and efficient. The everyday use, especially for boilerplate code or fast scripts, can be seen and understood.

But on closer inspection, and this is now also confirmed by studies (SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? (2025, February 24)) and field reports, this approach quickly reaches its limits.

Vibe coding: The glossy surface is deceptive

A recent study, which was reported here by the tech magazine Futurism (OpenAI Researchers Find That Even the Best AI Is "Unable To Solve the Majority" of Coding Problems), provides sobering results that many should confirm from practice. The study (which probably refers to a work by researchers who are also active at OpenAI) examined the capabilities of LLMs in real-world programming tasks:  

Superficial solutions: The models tested took on tasks worth hundreds of thousands of dollars on platforms like Upwork. The problem: They were often only able to fix surface-level software issues.

Lack of depth: At the same time, they remained unable to actually find bugs in larger projects or find their root causes.

Self-confident, but flawed: These "shoddy and half-baked solutions" are familiar to many who have worked intensively with AI. The models are great at spitting out confident-sounding information that often falls apart on closer inspection. This phenomenon is often referred to as a "hallucination".

Speed vs. understanding: Although the LLMs often operated "far faster than a human would", they lacked understanding. They did not capture the extent of errors or their context ("failed to grasp how widespread bugs were or to understand their context"). The result: "solutions that are incorrect or insufficiently comprehensive".

What does this mean for software development?

"Vibe coding" can lead us to be lulled into a false sense of security. Quickly generated code that works at first glance can obscure deeper problems, accumulate technical debt, or even create new security gaps. AI often optimizes for the immediate requirement without considering the big picture, architecture, or long-term maintainability.

The danger is that less experienced developers or teams will take over these half-finished solutions under time pressure without sufficiently testing them. The result is code that may "vibrate" in the short term but leads to headaches in the long term.

"Read my Lips: There will be no agents."

These observations lead to a clear, if perhaps provocative, conclusion, which the original initiator of this post, Michael Seemann, formulated in his Newsletter 48/2025: "Read my Lips: There will be no agents." At least not in the sense that fully autonomous AI systems will be able to develop, debug and maintain complex software projects independently soon. The current LLMs lack fundamental skills:

Real understanding: You don't understand code on a semantic level like a human does. They recognize patterns, but don't understand intent or deeper logic.

Contextual awareness: Complex software consists of many interacting parts. LLMs struggle to grasp this global context.

Critical Thinking & Debugging: They lack the ability to systematically seek out errors, form hypotheses, and test. They often can't "think" outside the box of their training dataset.

Abstraction and architecture: Designing robust, scalable, and maintainable systems requires a level of abstraction and foresight that goes far beyond pattern recognition.

Conclusion: Tool Yes, replacement No

LLMs are without question powerful tools. They can increase productivity, help with learning, generate ideas, and automate repetitive tasks. "Vibe coding" can make sense within certain limits – as an assistant, not as a main developer.

But the idea that we can sit back and let AI do the complex work of software development is an illusion based on current capabilities. Human expertise, critical thinking and deep understanding of systems remain essential. The "vibes" of AI must always be grounded by human intelligence and care.

Related Links

• Six Main Issues of Vibe Coding — Why It Can’t Be Used Commercially
• The paradox of vibe coding: It works best for those who do not need it
• Vibe Coding is a Dangerous Fantasy
• AI Search Has A Citation Problem