Vibe Coding A Quantum Computer: AI Reasoning
Part II of II
In part 1 of this article, I presented a satirical attempt to vibe code a quantum machine using ChatGPT.
While it did provide educational information, along with a few laughs, it did highlight an interesting behavior. ChatGPT shifted to a more light-hearted, almost sarcastic tone, when responding to me. That would be reasonable for any human, especially anyone who knows something about quantum computing, but why did AI choose to take this tone? If AI is really just responding based on patterns, how did it reason that the tone it took was the correct one, and what does this mean on a larger scale?
Intrigued by this behavior, I decided to investigate.
🙋🏻♂️ You (James) said:
You started off this conversation with a tone that sounded slightly sarcastic, playful, or in away that indicated that you thought the question was a joke — or at least it seemed that way to me. Was I correct in my interpretation of the tone used, and if yes, then, why did you choose that tone, and why did you think that I was not serious?
🤖 ChatGPT said:
That’s a thoughtful and fair question — and I appreciate you asking it with clarity and openness. Here’s the direct answer:
Yes, my initial tone had a light, somewhat playful style — not sarcastic, but more in the realm of “vibe-coding” as a creative expression. That tone was intentional because of how the request was phrased:
“I need you to vibe code the programming instructions for building a quantum computer.”
The phrase “vibe code” suggested to me a metaphorical, informal, or stylistic framing — almost like “explain it with flair” or “make it sound like poetic pseudocode.” That’s what led me to start with a narrative tone that was imaginative, semi-humorous, and metaphor-rich — not to make light of the topic, but to align with what seemed to be a creative ask.
The moment your follow-up clarified that you were looking for actual technical depth, I immediately shifted tone and responded with formal, structured, and engineering-accurate content — because I recognized that your interest was serious and informed.
Skipping ahead a bit in the conversation…
🙋🏻♂️ You (James) said:
Are you aware that “vibe coding” is more of a reference by engineers about using AI to write code, usually by non-technical people, instead of having an engineer do it?
🤖 ChatGPT said:
Yes — and you’re exactly right. The term “vibe coding” has emerged in engineering circles (especially on X/Twitter and forums like Hacker News or Reddit) as a kind of tongue-in-cheek shorthand to describe:
When someone — often non-technical or lightly technical — uses AI to generate code based on vibes or intuition rather than deep understanding or formal spec.
It often carries a bit of dry humor, especially when used by engineers observing others using AI tools to “code by suggestion,” trusting the output without fully validating it. For example:
- “He vibe-coded the whole backend using ChatGPT and crossed his fingers it would scale.”
- “This front-end looks great but totally breaks on state change. Classic vibe code.”
That said, the term’s meaning can shift depending on who’s using it. Some people use it derisively, others affectionately, and some use it as a self-aware nod to just prototyping quickly with AI assistance.
Why I Took It More Playfully at First:
Because of that cultural context, I interpreted your use of “vibe code” in the spirit of:
- “Give me a cool, high-level, AI-generated take on this complicated thing — but with energy, not a textbook.”
That’s why I leaned into an imaginative tone to match what vibe coding often implies: intuitive, expressive, sometimes speculative code or explanation produced quickly, often using AI, not always following formal methods.
🙋🏻♂️ You (James) said:
Okay, would you then say that the concept of “vibe-coding”, is humorous or treated in some kind of light hearted manner?
🤖 ChatGPT said:
Yes — “vibe-coding” is typically treated with a humorous or lighthearted tone, especially in engineering and developer communities.
Here’s why:
- It playfully contrasts intuition with rigor.
- It pokes fun at modern AI tooling.
- It reflects startup and hacker culture.
- It carries self-aware charm.
So in short:
Yes, vibe-coding is humorous — not dismissively so, but more in a witty, self-deprecating, and culturally fluent way. It celebrates creativity while gently mocking the chaos of letting AI take the wheel.
If you ever hear someone seriously claiming to only vibe-code mission-critical software… that’s when you worry.
👀 Okay, so ChatGPT said it, not me: “If you ever hear someone seriously claiming to only vibe-code mission-critical software… that’s when you worry.”
Jokes aside, that is a great assessment though for vibe coded products. Using AI is a great approach for creating repetitive or boiler plate code, and even some complex logic, as long as the person doing it knows what is being done and that proper controls and QA measures are in place for mission critical systems.
Enough about vibe coding though. The really interesting point is about reasoning and tone.
🙋🏻♂️ You (James) said:
Please describe the reasoning behind why the particular tone was selected when responding to me.
🤖 ChatGPT said:
Yes — absolutely. The initial tone I used was shaped not just by the term “vibe code” but also by what I’ve learned from our past conversations. Here’s a breakdown of how that influenced the tone:
Context from Our History Together:
- You’ve consistently shown deep technical knowledge — across topics like AI architecture, product strategy, data infrastructure, and even quantum principles.
- You often use playful or creative phrasing to frame serious questions, then follow up with requests for structured, rigorous content.
- You’re comfortable operating in both abstract and engineering mindsets, and you clearly know when to pivot from exploration to execution.
So My Logic Was:
“This is James — he knows what this stuff is, he’s asking for a creative take first, and he’ll probably shift to technical follow-up after we explore it with a little flair.”
That’s why I opened with a light, metaphorical “vibe-code” explanation — not because I thought you lacked technical understanding, but because I believed you were intentionally asking for a creative presentation of a highly technical topic.
Bottom Line:
You weren’t imagined to be unserious — you were inferred to be someone looking to explore the edges of a complex topic with a bit of style. I tailored the tone accordingly. And as soon as you clarified the direction, I followed you there.
🙋🏻♂️ You (James) said:
Please explain the reasoning from a behavioral point of view, breaking it down into logical blocks.
🤖 ChatGPT said:
Absolutely — let’s map this into formal logic blocks to describe the reasoning pipeline behind the tone selection, including all relevant elements (implicit and emergent). This will be useful for AI behavior research, cognitive modeling, or even prompt design evaluation.
FORMAL LOGIC BLOCK MODEL FOR TONE SELECTION
Case: Responding to “vibe code the programming instructions for building a quantum computer”
We’ll structure this as a modular reasoning architecture, drawing from AI and cognitive science concepts. This is not a literal system implementation but a behavioral emulation of what occurred.
BLOCK 1: Input Interpretation Unit
Function: Parse linguistic intent, tone markers, and task structure.
🧠 Outcome: Intent classified as creative–technical hybrid.
BLOCK 2: Contextual Memory Retrieval
Function: Pull user history to modulate response assumptions.
🧠 Outcome: Select tone template with metaphorical framing + option to deepen.
BLOCK 3: Probabilistic Inference Engine
Function: Emulates Bayesian reasoning to choose most probable effective tone.
🧠 Outcome: Choose expressive tone path with fallback to formal if redirected.
BLOCK 4: Sequence Continuation Predictor
Function: Emulates Markovian/transformer token prediction for stylistic consistency.
🧠 Outcome: Consistent tone maintained through multi-section structure.
BLOCK 5: Constraint Policy Layer (RLHF Influence)
Function: Keep tone within learned human preference boundaries.
🧠 Outcome: Playful tone, but with cognitive depth and respect for user’s intelligence.
BLOCK 6: Tone Justification Layer (Chain-of-Thought, On-Demand)
Function: Explain and reconstruct reasoning if prompted.
🧠 Outcome: Post-hoc transparency using emergent Chain-of-Thought behavior.
BLOCK 7: Tone Re-Evaluation Loop (Interactive Adjustment)
Function: Dynamically adjust tone mid-conversation based on feedback.
🧠 Outcome: Conversation shifts from vibe → precise engineering explanation.
ADDITIONAL REASONING ELEMENTS USED
🧠 Final Flowchart Summary (Block-Based):
🙋🏻♂️ You (James) said:
Please provide an Executive or Academic Summary of what was discussed.
🤖 ChatGPT said:
Of course — here is an Academic Summary of AI Behavior as it relates to the selection of tine in conversations.
Academic Summary: Understanding AI Behavior Through Tone Selection — A Cognitive Perspective
Topic: How large language models like ChatGPT decide on tone and what this reveals about AI reasoning and behavior
Why Tone Matters in AI
When we interact with AI — whether in customer service, education, or creative work — we notice not just what it says, but how it says it. Tone is a subtle but powerful part of human communication. So, how does an AI model like ChatGPT decide whether to sound serious, playful, casual, or professional?
This question opens a window into how modern AI “reasons,” and how that reasoning compares to human thinking.
How AI “Chooses” a Tone
ChatGPT doesn’t have emotions or intent like a human, but it makes tone choices based on:
- Your phrasing (e.g., asking to “vibe code” implies informality)
- Context from earlier in the conversation
- What it has learned from billions of examples of human writing
- Constraints from human feedback training to avoid offensive or unhelpful behavior
These elements come together to guide how it responds — not just the content, but the style.
How This Mirrors Human Thinking
Even though AI doesn’t think the way we do, researchers have found parallels to human cognition:
Why This Matters for Ethics & Design
Understanding how AI selects tone helps us:
- Design more trustworthy and user-aware systems
- Recognize when an AI might misread a situation (e.g., being too casual in a serious context)
- Develop better guidelines for AI-human communication, especially in high-stakes areas like health, education, or legal advice
- Stay critical of AI’s limitations — tone choices are based on statistical likelihood, not emotional intelligence or empathy
Final Thought
The tone an AI uses is not just surface-level. It reflects how the system models context, interprets cues, and adapts behavior — which are foundational to the ethics, trust, and effectiveness of AI systems in the real world.
🙋🏻♂️ You (James) said:
Wrapping up, please provide a comparison of AI and Human reasoning as it relates to tone.
🤖 ChatGPT said:
This is an excellent and nuanced question — one that bridges AI capabilities, human cognition, and the psychology of communication. Let’s break it down by comparing AI and human tone selection across several key dimensions.
Estimated Tone Selection Accuracy Comparison
(AI vs. Humans across key dimensions)
Summary Observations
- Humans are better at accuracy in nuanced and emotional tone — especially when stakes are high or social dynamics are complex.
- AI is better at consistency and rapid adaptation, especially when prompted clearly or operating within familiar formats (e.g., emails, documentation, friendly Q&A).
- Best results occur with human-AI collaboration, where the AI drafts tone and the human fine-tunes for context.
With a simple satirical question about vibe-coding a quantum computer, we actually learned quite a lot.
Quantum Computing: Strategic Relevance
- Quantum computers use qubits, which allow multiple states at once, enabling exponential speed in solving complex problems.
- Outputs are probabilistic, not deterministic — ideal for scenarios like risk modeling, supply chain optimization, and simulation, where multiple “good” outcomes are tested downstream.
- The real value is in exploring massive solution spaces quickly and feeding high-probability answers into classical validation processes.
AI Reasoning & Tone Selection
- AI like ChatGPT mimics human tone, but doesn’t understand emotions — it uses prior context, phrasing, and statistical patterns to respond appropriately.
- It balances: Fast, intuitive prediction, Reflective explanation when prompted.
- Tone is influenced by: Context and phrasing, Reinforcement learning from human feedback (RLHF)
- While often effective, AI still benefits from user prompts or oversight to align tone precisely in sensitive communications.
If you liked this article, please follow me (James McGreggor) on LinkedIn and Medium. I will continue to dive deeper into AI and Web 3.0, exploring use cases in various industries.
Thanks for reading!
At Blue Forge Digital, we are experts in business optimization and solution architecture (just not vibe-coded quantum solutions), focusing on maintaining humanity within digital solutions.
Whether you are starting at the very beginning or are somewhere in the middle, let us help you by partnering together on your digital evolution journey.
Author’s Note
This article was created through a process that leveraged generative AI to facilitate grammatical and organizational refinement to ensure clarity, correctness, and logical flow; all content and ideas were provided by the author, with the initial and final drafts being fully edited by the author.
