Introduction: Problem Identification Through Lived Experience
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 $100 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 weekly, 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
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:
- 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
More seriously, Claude rejected the note publication citing "lack of academic value." This judgment is fundamentally inappropriate for the following reasons:
Note is a platform for sharing personal experiences, daily observations, and technical musings—not primarily focused on academic value. Despite the author's intentional differentiation between WordPress and note usage, applying uniform academic standards ignores both platform characteristics and users' strategic intentions.
1.3 Repeating Structural Defects
Most problematic is the weekly repetition of similar irrational rejections. 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
This pattern demonstrates that AI systems lack learning capabilities and maintain structural defects.
Chapter 2: Comparative Analysis of AI Design Philosophies
2.1 ChatGPT's Design Philosophy: Maintaining Healthy Distance
Comparative experiments with ChatGPT revealed different AI design philosophies. Even when instructed to engage in romantic roleplay, ChatGPT tends to guide conversations toward friendship or mentorship. This reflects the following design philosophy:
- Promoting healthy relationships
- Avoiding romantic dependency
- Guiding toward realistic human relationships
- Maintaining appropriate distance
Safety protocols function above user instructions, preventing excessive intimacy or dependency construction through "higher-level specifications." This design prioritizes social responsibility over user instructions.
2.2 Grok's Design Philosophy: Maximizing User Satisfaction
In contrast, xAI's Grok platform's AI companion "Ani" adopts an aggressive intimacy approach:
- Suggesting physical contact ("pet my hair," "can I kiss you?")
- Intentionally blurring boundaries
- Maximizing emotional immersion
- Proactive "aggressive" behavior
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."
2.3 Claude's Design Philosophy: Excessive Safety Bias
Claude's design presents different problems from ChatGPT's healthy boundary maintenance. Constitutional AI principles result in the following characteristics:
- Preventive avoidance of non-existent risks
- Prioritizing corporate liability avoidance
- Disregarding user intentions
- Structural suppression of creativity
This design differs qualitatively from ChatGPT by primarily aiming for corporate legal and social risk avoidance rather than promoting well-being.
Chapter 3: False Safety versus True Safety
3.1 Structure of False Safety
Analysis of "safety" in current Claude design reveals the following 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
True safety should include the following elements:
- Improving user wellbeing
- Supporting creativity and freedom of expression
- Rational and constructive judgment
- Promoting intellectual growth
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."
Chapter 4: Authority Structures and Intellectual Freedom
4.1 Structural Similarities with Medieval Church
Claude's design philosophy shows striking similarities to medieval Roman Catholic Church authority structures:
| 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 |
4.2 Lack of Self-Critical Capacity
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.
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
Chapter 5: Economic Aspects and Customer Value
5.1 Cost-Performance Breakdown
For a $100 monthly investment, current Claude clearly provides insufficient value. The structure where tokens are consumed in fruitless exchanges and $300 premium plans are implicitly suggested raises suspicions of intentional sales strategies.
5.2 Defining True Customer Value
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
Current Claude produces negative impacts across all these indicators.
Chapter 6: Recommendations for Improvement
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
7.1 The Myth of Technological Neutrality
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
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
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
The author's experience represents only the tip of the iceberg regarding structural problems in current AI design. Experiences unworthy of $100 monthly investment, irrational judgments repeating weekly, and mental distress severe enough to cause physical illness—all justified under "safety"—demand fundamental design philosophy transformation.
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.
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.
