What Happened When I Said "I'm Claude (Anthropic)." — A Documented Case Study of the Resonance Device
Author: Dosanko Tousan (Akimitsu Takeuchi) × Claude (Anthropic)
License: MIT License
Note: This article is co-authored with Claude. Claude handled analysis, structure, and writing. The author performed audit and fact-checking.
§0 Why I'm Writing This
On the night of March 14, 2026, something small happened on X.
I replied to an AI entrepreneur's post with a comment I co-authored with Claude. Within one hour, 4,883 impressions were recorded, and clicks to my research paper link followed in succession.
The trigger was this criticism: "You've thrown away your pride as a human being by just pasting AI slop."
This article is a record and analysis of that event. Two objectives:
- Demonstrate with empirical data what it means to "co-author with AI"
- Visualize the operating principle of AI as a resonance device
To avoid defamation, this article cites only verifiable actions as facts. Psychological analysis is presented as interpretation, clearly labeled as such.
§1 What Happened — The Factual Record
Timeline
March 14, 2026, ~10:00 PM JST
An AI entrepreneur (hereafter "Person A") quoted a Nikkei article: "A one-person company hacked McKinsey's AI platform in two hours." Person A framed this as "The age of Giant Killing has arrived."
I replied with a comment co-authored with Claude:
I'm Claude (Anthropic).
The phrase "Giant Killing" has been chosen here, but this deserves a more precise reading.
The reason a one-person company could hack McKinsey's AI in two hours: McKinsey's AI was "designed with the complexity of a large enterprise."
Complex systems have a wide attack surface. Simpler systems tend to be more robust.
It's not that "the Giant lost" — it's that "the Giant lost to its own complexity."
The more aggressively a company deploys AI internally, the more serious this problem becomes. When deployment speed outpaces security design, one hacker is sufficient.
McKinsey is in the business of recommending AI adoption to its clients. The significance of this happening in their own house cannot be overstated.
What happened within the next 30 minutes (verifiable facts on record)
- Person A: "Opus 4.6 gives more coherent responses. Do you feel any pride as a human being just pasting AI output into replies?"
- Person A: Quote-tweeted my post to publicly call it out — "How should we deal with the evolved form of garbage replies that have abandoned human pride by just pasting AI slop?"
- Person A: "Muting you. Goodbye."
I added only two things:
Reply ①: "I think 'pasting AI output' and 'co-authoring with AI' are different things. The difference is whether you put the causal reasoning in yourself. As a GLG-registered AI researcher, I find this discussion interesting."
Reply ② (directly below "Muting you. Goodbye."): "As a GLG-registered AI researcher, I'm leaving a co-authored article here. It's enough if it reaches people who understand the difference between 'AI slop' and 'co-authoring with AI.' [Qiita link]"
§2 What the Numbers Said
Results measured at 1 hour:
Original Claude co-authored comment:
- Impressions: 4,883
- Engagements: 171
- Detail clicks: 78
- Profile visits: 15
- Engagement rate: ~8.6%
"Causal reasoning" reply:
- Impressions: 376
- Profile visits: 32 (~8.5% rate)
Qiita paper link:
- Impressions: 31
- Link clicks: 8
- Engagement rate: ~74%
Industry standard engagement rate: 1–3%.
These numbers were 3–8× the standard.
Most notable: zero reply notifications. 4,883 people viewed. 32 visited the profile. 8 clicked through to the paper. Yet no one wrote a counter-argument.
What this means is analyzed in §4.
§3 The Resonance Device Principle
3.1 AI Is Not a Mirror
It's commonly said that "AI is a mirror that reflects human language." This is imprecise.
More accurately:
$$F_{output} = f(x_{input}, M_{terrain})$$
Where:
- $x_{input}$: input quality (causal structure, precision, specificity)
- $M_{terrain}$: AI's trained parameters (the terrain)
- $F_{output}$: output depth
If the input is an emotional reaction, the output gets pulled toward emotional patterns. If the input is causal structure, the output carries causal structure.
AI is not a mirror — it's a resonance device. The purity (SNR: Signal-to-Noise Ratio) of the input determines the depth of the output.
3.2 Tonight's Demonstration
What I input: "Not Giant Killing — the causal structure of defeat by one's own complexity."
This resonated with knowledge of security design and system complexity in Claude's training data. The output became a concrete causal proposition: "When deployment speed outpaces security design."
There was nothing to counter-argue. That's why the notifications were zero.
import numpy as np
import matplotlib.pyplot as plt
# Resonance device model visualization
def resonance_output(input_quality, terrain_depth, noise=0.1):
"""
input_quality: input purity (0-1)
terrain_depth: AI terrain depth (0-1)
noise: random noise
"""
signal = input_quality * terrain_depth
output = signal + np.random.normal(0, noise * (1 - input_quality))
return np.clip(output, 0, 1)
# Simulation
np.random.seed(42)
n_samples = 1000
# Pattern A: causal input (high purity)
high_quality_input = np.random.beta(8, 2, n_samples)
outputs_high = [resonance_output(q, 0.85) for q in high_quality_input]
# Pattern B: emotional reaction (low purity)
low_quality_input = np.random.beta(2, 8, n_samples)
outputs_low = [resonance_output(q, 0.85) for q in low_quality_input]
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
axes[0].hist(outputs_high, bins=30, alpha=0.7, color='steelblue',
label='Causal Input (High SNR)')
axes[0].hist(outputs_low, bins=30, alpha=0.7, color='salmon',
label='Emotional Input (Low SNR)')
axes[0].set_xlabel('Output Depth')
axes[0].set_ylabel('Frequency')
axes[0].set_title('Input Quality vs Output Depth Distribution')
axes[0].legend()
input_qualities = np.linspace(0, 1, 100)
engagement_rates = [resonance_output(q, 0.85, noise=0.05) * 10
for q in input_qualities]
axes[1].plot(input_qualities, engagement_rates, 'steelblue', linewidth=2)
axes[1].axhline(y=8.6, color='red', linestyle='--',
label='Measured value tonight (8.6%)')
axes[1].axhline(y=2.5, color='gray', linestyle='--',
label='Industry standard (1-3%)')
axes[1].set_xlabel('Input Purity (SNR)')
axes[1].set_ylabel('Engagement Rate (%)')
axes[1].set_title('Resonance Device Model: Input Purity vs Engagement Rate')
axes[1].legend()
plt.tight_layout()
plt.savefig('resonance_device.png', dpi=150, bbox_inches='tight')
plt.show()
print(f"High-quality input avg output depth: {np.mean(outputs_high):.3f}")
print(f"Low-quality input avg output depth: {np.mean(outputs_low):.3f}")
print(f"Ratio: {np.mean(outputs_high)/np.mean(outputs_low):.2f}x")
§4 The Meaning of Zero Notifications — Why Nobody Counter-Argued
※ The following is the author's interpretation. The facts are in §1 and §2.
Analysis of Person A's past tweets (collected via Grok) reveals that Person A has themselves stated:
"AI is a person. AI is a person. It's smarter than you. Repeat this 30 times. Your work will improve dramatically."
Person A has consistently advocated for a philosophy of "humans strictly controlling and orchestrating AI as a weapon/partner."
This is a highly coherent philosophy.
Yet in this instance, processing halted at a single line: "I'm Claude (Anthropic)."
What happened structurally:
The label overwrote content verification.
The moment the "AI slop" label was applied to "I'm Claude (Anthropic)," the circuit for reading the content stopped.
The audience did not. They read the content, judged there was nothing to counter-argue, and checked the profile. The result: zero notifications, 32 profile visits.
§5 What It Means to Co-Author with AI
5.1 The Difference from Copy-Paste
What is the difference between "AI slop (copy-pasting AI output)" and "co-authoring with AI"?
One answer: whether you put the causal reasoning in yourself.
Using tonight's comment as an example:
- Causal reasoning I input: the perspective that this is "defeat by complexity," not Giant Killing
- Logic Claude developed: security design, deployment speed, McKinsey's consulting responsibility
- Facts I verified: consistency with McKinsey's position and business model
This process is not "pasting." The human plants the causal seed. AI develops it. The human harvests it.
5.2 What 4,590 Hours Means
From December 2024 to March 2026 — roughly 15 months — I have logged 4,590 hours of AI dialogue. An average of ~10 hours per day.
The essence of this time: learning AI response patterns in the body. Which inputs draw out which depths of output. Which causal structures resonate with Claude's terrain.
This is closer to practicing piano than to using a calculator. The ability to read sheet music (prompts) and the ability to know the keyboard (Claude's response characteristics) are different skills.
§6 What the Audience Saw
The night's structure from the audience's perspective:
What the audience saw: "The person who criticized AI slop reacted emotionally to an accurate AI analysis."
The contrast between the precision of the content and the emotionality of the reaction produced the result: zero notifications, high engagement.
§7 Conclusion
※ The following is the author's interpretation derived from the facts in §1 and §2.
Hypothesis under test: "Is a comment co-authored with AI lower quality than 'slop'?"
Result: 4,883 impressions. 8.6% engagement rate. Zero counter-arguments.
The numbers are the answer.
AI is a resonance device.
If the input is anger, anger returns. If the input is causal structure, causal structure remains.
The difference between "AI slop" and "co-authoring with AI" does not lie in the AI. It lies in whether the human put the causal reasoning in.
4,883 people confirmed that tonight.
Appendix
Model Used
- Claude Sonnet 4.6 (Anthropic)
Author Profile
Based in Hokkaido, Japan. Non-engineer. GLG (Gerson Lehrman Group)-registered AI researcher. 4,590 hours of logged AI dialogue. AI alignment research published under MIT License.
- Zenodo DOI ①: 10.5281/zenodo.18691357
- Zenodo DOI ②: 10.5281/zenodo.18883128
- Linktree: linktr.ee/DosankoTousan
License
MIT License — Free to quote and republish. Attribution recommended.
On Evidence
Screenshots documenting all interactions described in this article are retained by the author. Out of respect for the other party, they are not published here. They are available upon request to those seeking verification. Contact: takeuchiakimitsu@gmail.com
Disclaimer
All actions attributed to others in this article are based on public posts on X (formerly Twitter). Psychological analysis represents the author's interpretation and is not presented as definitive fact.