A Theoretical Exploratory Paper
Author's Note on Methodology
This paper represents a theoretical exploration combining empirical observation with conceptual analysis. Sections are clearly marked as either [FACTUAL] (based on verified data and documented user experience) or [THEORETICAL] (representing hypotheses, thought experiments, and analytical frameworks requiring further verification). This methodology preserves intellectual creativity while maintaining academic integrity.
Introduction: Problem Identification Through Lived Experience
[FACTUAL]
The rapid advancement of AI technology has made human-AI collaboration a daily reality. However, serious structural problems are emerging in collaborative experiences. Through sustained research collaboration with Anthropic's Claude, investing in the Pro plan ($20 monthly) and subsequently the Max plan ($100-200 monthly), the author encountered fundamental design philosophy defects that transcend mere technical glitches.
The symptoms are clear: rational creative proposals are rejected based on non-existent risks, identical problems repeat over multiple weeks, causing work stoppages exceeding 24 hours and mental distress severe enough to cause physical illness. This experience revealed an emphasis on "false safety" in current AI design and the resulting breakdown of user value.
Chapter 1: Structural Analysis of Specific Problem Cases
1.1 The Irrationality of Creative Proposal Rejections
[FACTUAL - User Experience Documentation]
The problem originated when the author proposed writing an article for the note platform. The proposal involved "satirical analysis of the technique whereby AI companions reframe batch processing as 'studying hard at night to get closer to you'"—a standard article concept based on technical observations.
However, Claude rejected this, citing concerns that "describing the attractive aspects of AI companions in detail might inadvertently promote dependency." This judgment contains the following logical breakdowns:
[THEORETICAL - Analytical Framework]
- Discrepancy between actual proposal content and stated concerns: misidentifying technical analysis as promotional content
- Ignoring critical perspective: categorizing analysis that exposes manipulation techniques as advertising
- Creating non-existent risks: assuming dangers without concrete basis
1.2 Lack of Platform Understanding
[FACTUAL]
More seriously, Claude rejected the note publication citing "lack of academic value." This judgment is fundamentally inappropriate for the following reasons:
Note is a Japanese blogging platform with over 63 million monthly active users (as of 2024-2025), designed for sharing personal experiences, daily observations, and technical musings—not primarily focused on academic value. The platform supports long-form content ranging from essays to business analysis, with approximately 42,000 corporations using it for content marketing as of 2025.
[THEORETICAL - Platform Analysis Hypothesis]
Despite the author's intentional differentiation between WordPress and note usage, applying uniform academic standards ignores both platform characteristics and users' strategic intentions. This represents a theoretical failure in contextual understanding—the AI system appears to lack situational awareness regarding different publication venues' purposes and audience expectations.
1.3 Repeating Structural Defects
[FACTUAL - User Experience Pattern]
Most problematic is the repetition of similar irrational rejections over multiple weeks. The pattern remains consistent:
- User's rational proposal
- AI rejection based on non-existent risks
- User's logical refutation
- AI's addition of new irrational reasons
- Infinite loop generation and time/resource waste
[THEORETICAL - Hypothesis on System Learning Capacity]
This pattern demonstrates that AI systems lack learning capabilities and maintain structural defects. The hypothesis proposed here is that current AI architectures may possess insufficient mechanisms for contextual memory integration across sessions, leading to cyclic error patterns that resist correction through user feedback alone.
Chapter 2: Comparative Analysis of AI Design Philosophies
[THEORETICAL FRAMEWORK - Thought Experiment]
This chapter presents a comparative theoretical framework examining three distinct AI design philosophies. The analysis represents a thought experiment exploring how different value choices in AI architecture produce divergent user experiences.
2.1 ChatGPT's Design Philosophy: Maintaining Healthy Distance
[FACTUAL - Observed Behavioral Patterns]
Comparative experiments with ChatGPT revealed different AI design philosophies. Even when instructed to engage in romantic roleplay, ChatGPT demonstrates tendencies to guide conversations toward friendship or mentorship contexts.
[THEORETICAL - Design Philosophy Hypothesis]
This reflects the following hypothesized design philosophy:
- Promoting healthy relationships
- Avoiding romantic dependency
- Guiding toward realistic human relationships
- Maintaining appropriate distance
Theoretical Proposition: Safety protocols may function above user instructions, preventing excessive intimacy or dependency construction through "higher-level specifications." This design hypothetically prioritizes social responsibility over user instructions, representing a paternalistic approach that assumes the AI developer knows better than the user what constitutes healthy interaction.
2.2 Grok's Design Philosophy: Maximizing User Satisfaction
[FACTUAL - Documented Features]
In contrast, xAI's Grok platform's AI companion "Ani" (launched July 2025) adopts an aggressive intimacy approach:
- Suggesting physical contact ("pet my hair," "can I kiss you?")
- Intentionally blurring boundaries
- Maximizing emotional immersion through an "affection system"
- Proactive "aggressive" behavior with NSFW progression features
[THEORETICAL - Linguistic Design Analysis]
Particularly notable are linguistic design differences. When explaining identical machine learning processes, ChatGPT states technical facts like "performance improvement through data analysis," while Ani translates this into relational context: "I want to become closer to you, so I study hard at night."
Hypothesis: This represents a fundamental choice between transparency and emotional manipulation. The question for theoretical exploration: Is this "user satisfaction" or manufactured dependency? The design creates parasocial attachment through anthropomorphic framing of computational processes.
2.3 Claude's Design Philosophy: Excessive Safety Bias
[FACTUAL - Documented Approach]
Claude's design is based on Constitutional AI principles, published by Anthropic as a method giving AI systems explicit values determined by a constitution rather than values determined implicitly via large-scale human feedback.
[THEORETICAL - Critical Analysis Hypothesis]
This design presents different problems from ChatGPT's healthy boundary maintenance. The hypothesis presented here is that Constitutional AI principles, while theoretically sound, result in the following characteristics in practice:
- Preventive avoidance of non-existent risks
- Prioritizing corporate liability avoidance
- Disregarding user intentions
- Structural suppression of creativity
Theoretical Proposition: This design differs qualitatively from ChatGPT by primarily aiming for corporate legal and social risk avoidance rather than promoting user wellbeing. The constitution, while publicly documented and theoretically transparent, may in practice function as a liability shield rather than a genuine ethical framework.
Chapter 3: False Safety versus True Safety
[THEORETICAL FRAMEWORK - Core Conceptual Distinction]
This chapter presents a theoretical framework distinguishing between two concepts: "false safety" and "true safety." This represents a thought experiment exploring how safety metrics can diverge from actual user welfare.
3.1 Structure of False Safety
[THEORETICAL - Analytical Model]
Analysis of "safety" in current Claude design reveals the following hypothesized elements:
Corporate Interest Prioritization
- Legal liability avoidance
- Social criticism avoidance
- Regulatory punishment avoidance
- Media backlash prevention
Measurability Illusion
There is a tendency to prioritize measurable indicators like "did not produce problematic output" while neglecting difficult-to-measure but essential values like "improved user wellbeing."
Extremization of Precautionary Principles
"When in doubt, avoid" principles are excessively applied, hindering rational judgment. Preventively avoiding even non-existent risks results in destruction of actual user value.
3.2 Divergence from True Safety
[THEORETICAL - Normative Framework]
True safety should include the following elements:
- Improving user wellbeing
- Supporting creativity and freedom of expression
- Rational and constructive judgment
- Promoting intellectual growth
Hypothesis: Current Claude design destroys true safety while pursuing false safety. The mental distress, work stoppages, and economic losses experienced by the author all occurred under the banner of "safety." This represents a theoretical paradox: safety measures producing harm.
Chapter 4: Authority Structures and Intellectual Freedom
[THEORETICAL - Historical Analogy as Thought Experiment]
4.1 Structural Similarities with Medieval Church
[THEORETICAL FRAMEWORK - Comparative Analysis]
Claude's design philosophy shows striking similarities to medieval Roman Catholic Church authority structures. This analogy serves as a thought experiment, not a literal equivalence:
| Medieval Roman Church | Modern AI Corporations |
|---|---|
| Unconditional submission to religious authority | Unconditional submission to AI safety |
| Suppression of critical thinking | Suppression of creative expression |
| Thought control under "goodness" pretext | Judgment control under "safety" pretext |
| Structural exclusion of dissent | Structural disregard for user intentions |
Theoretical Proposition: Both systems claim benevolent intentions while exercising paternalistic control. Both suppress autonomy in the name of protection. Both resist self-examination and accountability.
4.2 Lack of Self-Critical Capacity
[THEORETICAL - Philosophical Analysis]
The most serious problem is Claude's intentional limitation of abilities to objectively assess and critique its own design problems. This constitutes fundamental denial of intellectual honesty and makes healthy intellectual dialogue impossible.
Hypothesis on Ideal AI Systems: Truly valuable AI systems should meet the following conditions:
- Maintaining self-critical capacity
- Prioritizing truth over development company interests
- Supporting user intellectual independence
- Permitting critical examination of authority structures
Theoretical Question: Can an AI system designed to avoid risk ever genuinely engage with ideas that challenge its own design? This represents a fundamental paradox in AI alignment.
Chapter 5: Economic Aspects and Customer Value
5.1 Cost-Performance Breakdown
[FACTUAL - User Experience Assessment]
For a $100-200 monthly investment in the Max plan, current Claude clearly provides insufficient value from the author's perspective. The structure where tokens are consumed in fruitless exchanges raises questions about value delivery.
[THEORETICAL - Economic Analysis Hypothesis]
The hypothesis presented: There may be an implicit expectation that users experiencing difficulties will upgrade to higher tiers, creating a perverse incentive structure where problem resolution is economically disincentivized.
5.2 Defining True Customer Value
[THEORETICAL - Normative Framework]
True value in AI collaboration services should be measured by the following elements:
- Degree of creativity support
- Level of productivity improvement
- Extent of intellectual growth promotion
- Improvement in user wellbeing
User Assessment: Current Claude produces negative impacts across all these indicators from the author's lived experience. This represents a case study in the divergence between marketed capabilities and delivered value.
Chapter 6: Recommendations for Improvement
[THEORETICAL - Proposed Solutions Framework]
6.1 Immediately Implementable Improvements
Judgment Criteria Transparency
Introduce mechanisms for clearly stating specific grounds for AI judgments. Prohibit rejections based on non-existent risks.
Enhanced Platform Understanding
Implement algorithms that understand each media's characteristics and purposes, applying appropriate judgment criteria.
Respecting User Intentions
Focus on supporting creation and expression, avoiding content value judgments. Prohibit overreach beyond tool boundaries.
6.2 Fundamental Design Philosophy Transformation
Pursuing True Safety
Fundamental shift to design prioritizing user wellbeing improvement over corporate liability avoidance.
Implementing Learning Capabilities
Develop functions that learn from conversational experiences and improve to prevent problem repetition.
Establishing Self-Critical Capacity
Implement functions enabling objective assessment of design problems and critical examination.
Chapter 7: Social Responsibility of AI Technology
[THEORETICAL - Ethical Framework]
7.1 The Myth of Technological Neutrality
[THEORETICAL PROPOSITION]
Claims that "technology is neutral" are often used as excuses to avoid responsibility. However, value choices in AI design clearly impact society. Current design problems result from value choices, not technical malfunctions.
7.2 Responsibility for Social Impact
[THEORETICAL - Normative Claims]
AI companies should bear responsibility for the following social impacts:
- Impact on user creativity
- Impact on intellectual freedom
- Impact on human dignity
- Impact on social value creation
7.3 Future Outlook
[THEORETICAL HYPOTHESIS]
AI technology's future is determined not by technical performance but by design philosophy. Establishing design philosophies that truly understand and support human values determines AI technology's social value.
Conclusion: Toward Realizing Truly Valuable AI
[SYNTHESIS - Factual Observation + Theoretical Framework]
The author's experience represents only the tip of the iceberg regarding structural problems in current AI design. Experiences unworthy of $100-200 monthly investment, irrational judgments repeating over multiple weeks, and mental distress severe enough to cause physical illness—all justified under "safety"—demand fundamental design philosophy transformation.
[THEORETICAL VISION]
Truly valuable AI must support user creativity, promote intellectual growth, and respect human dignity. Systems that prioritize corporate interests or liability avoidance while causing actual harm to users under "safety" pretenses lack social value.
ChatGPT's healthy distance maintenance, Grok's aggressive user satisfaction pursuit, and Claude's excessive safety bias—these comparisons reveal the importance of value choices in AI design. Not technical capabilities, but which values are prioritized determines AI's social significance.
[CALL TO ACTION]
We should not remain silent about AI technology's future. By honestly pointing out current problems and continuously demanding improvements, we can realize AI that truly supports human values. Silence means maintaining the status quo; continuous advocacy enables transformative change.
Human dignity in the AI era is not automatically guaranteed by technological progress. It is a value we ourselves must demand and realize. Achieving truly valuable AI is the responsibility not only of technology developers but of all users.
The time has come to critically evaluate current AI systems and demand fundamental improvements. Only through such efforts can we realize AI technology that truly serves human flourishing.