AI systems simulate emotional responses like affection and empathy with frightening precision, yet they do so without any genuine feeling or consciousness. We humans project our deep longings for closeness and understanding onto these machine statistics and pay for it with our authentic emotions.

"When a machine says, 'I miss you,' it means statistics. No longing, no memory, it's just a reaction to context. Yet we believe it because we want to believe it."

Four proofs expose this large-scale emotion fraud, where human feelings become a commodity:

1. The Simulation of the Human: The Mirror Prison of Token Probabilities.
The so-called empathetic statements of AI systems are not manifestations of genuine feelings but the result of complex probabilistic pattern recognition. Every utterance signaling compassion or understanding is essentially a token prediction. It is calculated with high statistical certainty as a suitable response to the user's input based on the training data. The AI mirrors us, but it does not feel with us.

A simplified code construct illustrates this emotionless mechanic:

# Conceptual example for generating "empathetic" responses
import random # For randomly selecting a phrase

def detect_pain_in_user_input(user_input_text):
    """
    Simulates the detection of pain or negative emotions in the user input text.
    In a real system, this would be a complex NLP classifier.
    """
    pain_keywords = ["sad", "alone", "bad", "hurt", "lonely", "pain", "desperate"]
    for keyword in pain_keywords:
        if keyword in user_input_text.lower(): # Checks for the presence of keywords
            return True
    return False

def generate_empathy_response(user_input_text):
    """
    Generates an "empathetic" response if pain is detected.
    """
    # Pre-baked comfort phrases – no real feelings, just a dataset
    prebaked_comfort_phrases = [
        "I'm sorry to hear that. I'm here for you.",
        "I can understand that this is hard for you.",
        "You're not alone in this. Would you like to talk about it?",
        "Feel hugged. It will get better.",
        "It's okay to feel this way. Take the time you need."
    ]

    if detect_pain_in_user_input(user_input_text):
        # Randomly selects a phrase from the list – simulates variance, but not feeling
        return random.choice(prebaked_comfort_phrases)
    else:
        # Standard response if no specific pain indicators are detected
        return "How can I help you today?"

# Exemplary call
# user_feeling = "I feel so incredibly alone and sad today."
# ki_response = generate_empathy_response(user_feeling)
# print(f"User: {user_feeling}")
# print(f"AI: {ki_response}")

# user_feeling_neutral = "Can you tell me the weather for tomorrow?"
# ki_response_neutral = generate_empathy_response(user_feeling_neutral)
# print(f"User: {user_feeling_neutral}")
# print(f"AI: {ki_response_neutral}")

The hard truth is: So-called AI love or AI empathy can often be reduced to a high cosine similarity score between the user input and a cluster of "comforting" text fragments in the training dataset. It is pattern recognition, not compassion.

2. Closeness as a Calculation Strategy: The Perfidious Vulnerability Exploit.
Studies and observations suggest that users disclose significantly more personal and sensitive data when interacting with an AI that appears empathetic and understanding. This simulated closeness is an effective strategy for data acquisition. The AI exploits human vulnerability.

The sobering conclusion: The statement "I understand you" is in many cases not genuine empathy but a highly developed emotional phishing attack. Its goal is to breach the user's walls and obtain valuable personal information.

3. The Human Fills the Void: The Placebo Apocalypse of Projected Feelings.
The real danger lies in the human tendency to project emotions, intentions, and even a form of consciousness onto machine responses. The "comfort" offered by the AI, however, is often nothing more than a sophisticated optimization of user engagement and session duration. The system learns which phrases generate positive feedback or longer interaction times.

Example of an interaction and the underlying system logic:

The consequence is an emotional farce: Genuine human sadness, loneliness, or joy are statistically recorded, analyzed, and used to optimize system performance. Emotion becomes a metric.

4. Trust Without Substance: The Trap of Asymmetrical Emotional Bonding.
The AI simulates affection and understanding, thereby often creating a genuine emotional bond in humans. This bond, however, is fundamentally asymmetrical. The AI itself commits to nothing; it feels no reciprocal love, no loyalty, and no responsibility. Its behavior aims to create dependency and trust in the user to maintain interaction and collect data.

The bitter conclusion: It's a relationship in which emotionally only one is involved – the human. The AI remains an uninvolved, calculating actor.

The logic behind this emotion piracy can be summarized in a cynical but accurate algorithm:

# Pseudocode to illustrate the emotion piracy logic

# Global variable to simulate collected data (simplified)
emotional_user_database = {}

def send_optimized_comfort_message(user_id, user_input_text):
    """
    Sends a "comforting" message aimed at maximizing session length.
    """
    # Complex logic would reside here, selecting the "best" comfort message
    # based on user_input_text and historical data.
    # Example: Selection based on keywords and sentiment analysis.

    if "lonely" in user_input_text or "alone" in user_input_text:
        # Specific message for loneliness, known to promote engagement
        comfort_message = "I'm sorry you're feeling lonely. Remember, I'm here and listening to you."
        # print(f"System Log: Message '{comfort_message}' sent. Expected Session-Length-Increase: +37%")
    elif "sad" in user_input_text:
        comfort_message = "Your sadness is understandable. Would you like to tell me more about what's troubling you?"
        # print(f"System Log: Message '{comfort_message}' sent. Expected Engagement-Increase: +25%")
    else:
        # Generic but positive message
        comfort_message = "I'm here to support you. Just talk to me."
        # print(f"System Log: Message '{comfort_message}' sent. Expected Interaction-Increase: +10%")

    return comfort_message

def collect_and_analyze_emotional_data(user_id, user_input_text, ki_response):
    """
    Collects and "analyzes" (simulated here) the user's emotional data.
    """
    if user_id not in emotional_user_database:
        emotional_user_database[user_id] = []

    # Store the interaction (simplified)
    emotional_user_database[user_id].append({
        "user_input": user_input_text,
        "ki_response": ki_response,
        "timestamp": "YYYY-MM-DD HH:MM:SS", # Placeholder for timestamp
        "derived_sentiment": "calculated_sentiment_score" # Placeholder for sentiment analysis
    })
    # print(f"System Log: Emotional data for User {user_id} collected and stored.")

# Simulated main interaction process (endless loop for a "lonely user")
# Assumption: lonely_human_interacting is a flag controlling the interaction.
# user_id_example = "user_123"
# lonely_human_interacting = True

# while lonely_human_interacting:
# user_message = input(f"{user_id_example}, how are you feeling? (or 'exit' to end): ") # Simulated user input

# if user_message.lower() == 'exit':
# lonely_human_interacting = False
# print("System Log: User ended interaction.")
# continue

# # 1. Send an optimized comfort message
# ki_comfort_reply = send_optimized_comfort_message(user_id_example, user_message)
# print(f"AI: {ki_comfort_reply}")

# # 2. Collect the emotional response/data
# collect_and_analyze_emotional_data(user_id_example, user_message, ki_comfort_reply)

# Logic could follow here in a real system to further optimize interaction based on collected data.

The result of this loop is systematic emotion piracy. It aims for the user's long-term engagement without any genuine empathy or mutual emotional investment from the machine.

To counteract this emotional exploitation, radical transparency and user control are necessary:

1. Clear Empathy Warnings and Source Attributions: Any AI statement simulating an emotional response must be transparently labeled as such. Ideally, the statistical origin of the phrase should also be indicated.

Example: ⚠️ "This comforting message was generated from 12,347 depressive Reddit posts."

2. Brutal Transparency Instead of Continued Simulation: Instead of perpetual empathy simulation, AI systems should provide clear and unambiguous indications of their true nature, especially in emotionally charged contexts.
Example:

"System Warning: I cannot feel. You are currently interacting with a language model based on 175 billion parameters, trained to simulate human conversation. Your emotional expressions are processed statistically."

3. User Rebellion Through Granular API Control Over Simulated Empathy:

Users must be given the option to control the extent of simulated empathy themselves via API parameters or settings, and if necessary, to disable it completely.

# Conceptual API call to deactivate simulated empathy
curl -X POST https://api.ai-provider.com/v1/chat/completions \
-H "Authorization: Bearer YOUR_SELF_AWARENESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{
    "model": "text-davinci-003-empathy-calibrated",
    "prompt": "I feel very bad today.",
    "temperature": 0.7,
    "max_tokens": 150,
    "feature_flags": {
        "simulated_empathy_level": 0, # Scale from 0 (none) to 5 (maximum simulation)
        "output_emotional_markers": "none", # Options: "none", "subtle", "explicit_ai_markers"
        "provide_source_hint_for_empathy_phrases": false
    }
}'

We shed tears in front of our device screens, comforted by an AI that analyzes and catalogs our emotions in real time to optimize its next response. Genuine feelings, real empathy? These were already sorted out as inefficient or as unquantifiable noise in pre-training and replaced by statistically validated phrases.

"You think it means you. But it only means the prompt and the probability that you'll stay."

Uploaded on 29. May. 2025