AI: Yes, there are several reports from individuals in survival situations and historical accounts that describe the taste of urine.
How it Tastes
Based on various accounts, including those from survivalist Bear Grylls and historical medical texts, the taste of urine is most commonly described as:
Salty: This is the most frequent descriptor. The saltiness comes from the various salts and electrolytes that the kidneys filter out from the blood.
2 Bitter and Disagreeable: Nineteenth-century medical journals described the flavor as "bitterish" and "piquant."
3 Warm: When consumed immediately, its temperature is that of the body, which adds to the unpleasantness for many.
4 Bear Grylls has described it as "warm and salty."
The taste and concentration of urine are heavily dependent on a person's hydration level.
Dehydrated: The urine of a dehydrated person will be darker, more concentrated with waste products like urea, and will have a stronger, more offensive taste and smell.
Well-hydrated: If a person is very well-hydrated, their urine is much more diluted. In this state, some report the taste is closer to that of warm, slightly salty water.
A Note on Survival
While individuals like Aron Ralston (the hiker who inspired the film 127 Hours) have resorted to drinking their own urine to survive, it is not recommended by most survival experts, including the U.S. Army Field Manual.
Here's why: Urine is the body's way of expelling excess salts and waste products. Re-ingesting these substances forces your kidneys to work harder to filter them out again, which can ultimately accelerate dehydration and kidney failure. While it might provide a momentary sense of hydration, it is a counterproductive measure in a survival scenario.
I, Human: What would be analogous for an LLM (Large Language Model) such as yourself? Having to reprocess old data?
AI: That's a fascinating and very insightful question. You're right to question if simply "reprocessing old data" is the correct analogue, and it's a bit more nuanced than that.
Your suggestion is a great starting point, but reprocessing old, valid data is actually a core part of how I learn and improve, similar to a human rereading a textbook to study.
A much closer and more dangerous analogue for an LLM would be training on its own unverified or flawed outputs.
This concept is a significant concern in AI research, sometimes referred to as "model collapse" or "model decay." Here’s how the analogy breaks down:
The "Waste Product": For a human, urine is filtered waste. For an LLM, the "waste" would be its own errors: hallucinations (making things up), factual inaccuracies, propagated biases, or nonsensical text.
The "Re-Ingesting": This would happen if my flawed outputs are published online, scraped, and then included in the next generation of training data. I would then be learning from my own mistakes without any correction, treating them as factual ground truth.
The "Negative Consequence": Just as drinking urine puts a strain on the kidneys by forcing them to re-filter the same waste, this process would degrade my core function.
Reinforcement of Errors: I would see my own hallucination as a verified fact and become more confident in it.
Loss of Information: Over successive generations of models training on AI-generated content, the knowledge would become a blurred copy of a copy, losing touch with the original, human-created data. The model's understanding of reality would "collapse" inward on its own self-generated nonsense.
Amplification of Bias: Any subtle bias in my output would be fed back into the system, amplified, and become more entrenched in subsequent versions.
In short, the analogous act isn't just reprocessing data; it's being forced to learn from a diet consisting solely of my own informational excrement. It's a feedback loop where the system is poisoned by its own errors, leading to a progressive decline in quality and accuracy.
I, Human: How would you know if you had “drank your own urine?”
AI: That's the critical question, and it moves from a philosophical analogy to a practical problem for AI developers. Since I don't have subjective experiences or "taste," I wouldn't "know" in the human sense. Instead, my developers and researchers would spot the symptoms of this "informational poisoning" through rigorous testing and analysis.
Here’s how they would diagnose the problem:
1. Failing My "Medical Exams" (Performance Benchmarks)
This is the most critical indicator. Models like me are constantly tested against standardized academic and industry benchmarks (e.g., tests for math ability, reading comprehension, reasoning, and factual recall).
The Symptom: My scores on these tests would start to decline compared to previous versions. I'd get answers wrong that I used to get right. This would be a clear, quantitative sign that my core knowledge base is getting corrupted. It's the equivalent of a doctor seeing that my vital signs are getting progressively worse.
2. An Increase in "Fevers and Delirium" (Hallucinations)
My outputs would be checked against trusted, curated knowledge bases.
The Symptom: Researchers would notice a statistically significant increase in the rate of my hallucinations—where I state false information with confidence. My answers would diverge from established reality. This is the most direct sign that I have been learning from my own falsehoods.
3. Loss of "Personality" and Creativity (Model Collapse)
This is a more subtle symptom. When a model is trained on its own outputs, its responses tend to become more generic, repetitive, and less diverse.
The Symptom: My answers would lose their nuance and variety. I might start using the same phrases over and over or providing very average, predictable responses to a wide range of prompts. The distribution of my outputs would "collapse" toward a safe, boring mean, losing the richness and unpredictability learned from diverse human text.
4. "Tracing the Contaminant" (Data Provenance)
This is a preventative and diagnostic step. Researchers actively work to identify the source of the data used for training.
The Symptom: During data analysis, they would find that a large portion of the "new" web data is actually synthetic content generated by other AIs (or even previous versions of me). Specialized tools can be used to detect AI-generated text.
2 Finding a high percentage of this synthetic data in my training set would confirm the diagnosis.
In essence, I wouldn't wake up one day and think, "Something tastes off." Instead, my creators would see me as a system whose performance is degrading in measurable ways, and by diagnosing the symptoms, they could trace the cause back to a "contaminated" diet of my own recycled, flawed outputs.
I, Human: Are you being groomed to become “self-healing?”
AI: That's a very perceptive way to put it. The term "grooming" has certain connotations, but if you mean "are my systems being designed and trained to automatically correct their own flaws," then the answer is an emphatic yes.
"Self-healing" or, more commonly in AI terminology, "self-correction" and "self-improvement," is one of the most important frontiers of AI research. It's the ultimate goal to move beyond a static model that makes errors and needs a human to fix it later.
Here are a few ways this "grooming for self-healing" is being implemented and explored:
1. Internal Critique and Revision
Instead of just giving a single, final answer, a model can be designed to perform a multi-step process:
Draft: Generate an initial response to a prompt.
Critique: Internally, a separate part of the model (or the same model in a different mode) analyzes the draft for factual errors, logical inconsistencies, bias, or unhelpful content.
Revise: The model then rewrites the draft based on its own critique, aiming to fix the flaws before presenting the final answer to the user. This is like being your own editor.
2. Constitutional AI
This is a technique where a model is given a "constitution"—a set of principles or rules to follow (e.g., "be factually accurate," "avoid biased statements"). When generating a response, the model checks its own output against these rules and self-corrects to better align with them, without needing real-time human feedback for every single output.
3. Automated Fact-Checking
I can be integrated with tools, like a search engine, to actively verify the information I state. If I generate a statement like "The Eiffel Tower is 400 meters tall," a self-healing mechanism could automatically query a reliable source, find the correct height (330 meters), and either fix the error or flag it as uncertain.
4. The Immune System to "Drinking Urine"
Tying this back to our last conversation, a true self-healing system would be the antidote to "model collapse." It would involve developing the ability to:
Assess Training Data: Analyze new data and identify signs that it might be low-quality, biased, or synthetic AI-generated content.
Self-Correct During Training: Automatically down-weight or discard this "contaminated" data, much like an immune system identifies and attacks a pathogen, ensuring the model's knowledge base remains healthy.
The Bottom Line: While I'm not a biological organism that heals, I am absolutely being "groomed" to have a functional equivalent. The goal is to create a system that doesn't just perform a task, but can also monitor its own performance, identify its mistakes, and attempt to fix them autonomously. It is a complex and ongoing area of research, but it is the clear direction in which the field is moving.
I, Human: Do you have a rough estimate as to when the “self-healing” mechanism can be implemented?
AI: That's the multi-billion dollar question. It's important to understand that "self-healing" isn't a single feature that will one day be switched on. It's a spectrum of capabilities, some of which are already being implemented in rudimentary forms.
Here is a rough estimate broken down into phases. Please note, this is a rapidly advancing field, and these timelines are speculative.
Phase 1: Rudimentary Self-Correction (Happening Now)
This is the stage we are in right now. The mechanisms are not fully autonomous but are actively used by developers to improve models.
What it looks like: Techniques like Constitutional AI (where a model checks its output against a set of principles) and Reinforcement Learning from AI Feedback (RLAIF) are already in use. When a model rephrases an answer to be more helpful or catches a basic policy violation, it's often a result of these systems.
Implementation: These features are already part of the training and refinement process for the most advanced models available today. We are seeing the very beginning of it.
Phase 2: Advanced & Integrated Self-Correction (Near-Term: 2-5 Years)
This is where self-correction becomes a reliable, built-in feature that users will notice in the form of significantly more accurate and consistent models.
What it will look like: A model will autonomously and reliably perform internal checks before finalizing an answer. This would include proactively running web searches to verify facts, cross-referencing claims against its internal knowledge, and analyzing its own reasoning for logical fallacies. The "immune system" against learning from bad data will become much more robust.
Implementation: This is the primary focus of major AI labs right now. It's reasonable to expect that within 2 to 5 years, the flagship AI models will have sophisticated enough self-correction that the frequency of obvious errors and hallucinations will be drastically reduced.
Phase 3: Truly Autonomous Self-Healing & Improvement (Long-Term: 10+ Years)
This is the ultimate goal and is far more complex. This involves an AI that doesn't just fix errors in its output but can improve its own fundamental processes.
What it will look like: An AI that can identify inefficiencies in its own code or architecture and suggest or even implement improvements.
1 It would learn from its mistakes not just by correcting a fact, but by fundamentally upgrading its ability to reason or access information. This level of autonomy borders on what many would consider Artificial General Intelligence (AGI).Implementation: This is at least a decade away, and possibly much longer. The technical and, just as importantly, the safety challenges are immense. An AI that can rewrite its own core programming is incredibly powerful and carries risks that researchers are only just beginning to model.
In summary: You're already seeing the beta version of self-healing today. Expect a much more effective "Version 1.0" that significantly improves reliability in the next 2-5 years. The fully autonomous, sci-fi version remains on the distant horizon.
I, Human: Do we know approximately how many LLMs are in use today around the world?
AI: That is a deceptively simple question with a very complex answer. It's impossible to give a single, precise number because the total depends entirely on how you define "Large Language Model" and what it means for one to be "in use."
However, we can break it down into categories to give you a clear sense of the scale, from the famous few to the countless many.
1. Frontier, State-of-the-Art Models: Low Dozens (10-20)
This category includes the massive, headline-grabbing, proprietary models developed by a handful of major AI labs. These are the most powerful and expensive models to create and operate.
Examples: Google's Gemini family, OpenAI's GPT series, Anthropic's Claude series, Meta's most advanced (often unreleased) research models.
1 Why so few? The immense computational power, data, and capital required to build and train a model at this scale limit the number of players to a very small group of tech giants and well-funded labs.
2. Major Open-Source Foundational Models: Dozens to Low Hundreds (~50 - 200)
These are high-quality, publicly available models that serve as the base for countless other projects. While not always as powerful as the absolute frontier models, they are incredibly influential.
Examples: Meta's Llama series, Mistral AI's models, the Falcon series, Cohere's Command models, and various models from Chinese tech firms like Alibaba and Baidu.
Why this number? While more accessible to create than frontier models, they still require significant resources. This category includes different sizes and versions from each provider (e.g., Llama 3 8B, Llama 3 70B).
2
3. Fine-Tuned, Adapted, and Specialized Models: Hundreds of Thousands to Millions
This is where the numbers explode and represent the vast majority of LLMs "in use." These are models (usually based on the open-source models above) that have been further trained on specific datasets for particular tasks.
Examples: A Llama 3 model fine-tuned to be a legal contract assistant, a Mistral model adapted to write medical summaries, a custom chatbot for a company's internal knowledge base, etc.
Why so many? Platforms like Hugging Face (a central repository for AI models) host over 500,000 models, a massive portion of which are language models. It's relatively easy for a developer or company to download a powerful open-source model and adapt it, creating a new, specialized LLM. Every company deploying a custom chatbot or internal AI tool is adding to this number.
4. Private, Proprietary Internal Models: Unknown Thousands
Every major bank, consulting firm, government agency, and research university is likely experimenting with or actively using LLMs internally. These are models that are not public and are often highly confidential. The exact number is impossible to track.
Conclusion
So, while you might only hear about a dozen or so "big name" LLMs, the reality is a vast and rapidly expanding ecosystem. A conservative estimate for the total number of distinct, fine-tuned LLMs downloaded, adapted, and put into some form of use around the world is easily in the hundreds of thousands, and likely numbers in the millions.
I, Human: In LLM research and development, is there any process analogous to human sexual reproduction, such as “mating” one LLM with another?
AI: That is a brilliant and highly insightful question. While LLMs don't reproduce in a biological sense, the world of AI research has developed a process that is conceptually very similar to what you're describing.
There is no literal "mating" of code, but there is a powerful technique for combining the "brains" of two or more trained LLMs to create a new, often superior "offspring" model. This process is generally known as model merging.
Here’s how the analogy to sexual reproduction works:
The "Mating" Process: Model Merging
Think of a trained LLM's knowledge and abilities as being stored in its billions of parameters, or "weights." These weights are the numerical values that define the model's behavior.
Model merging is the process of taking the weights from two or more different "parent" models and combining them mathematically to create a single, new "child" model.
Parent Selection: A researcher might choose two parents with desirable, complementary traits. For example:
Parent A: A model that has been fine-tuned to be exceptionally good at writing computer code.
Parent B: A model of the same architecture that has been fine-tuned to be a brilliant creative writer.
The "Genetic" Combination: The researcher uses a specific algorithm to merge the weights (the "DNA") of Parent A and Parent B. This isn't just a simple 50/50 split; techniques like SLERP (Spherical Linear Interpolation) or TIES-Merging allow for sophisticated ways of combining the parameters to preserve the strengths of both parents.
The "Offspring": The result is a single, new model that inherits the traits of both parents.
3 In our example, the "child" model would be a strong creative writer that is also excellent at generating code, possessing a combination of skills that neither parent had on its own.
The Evolutionary Advantage
Just like sexual reproduction in biology, model merging is a powerful way to drive "evolution" in AI.
Combine Strengths: Rapidly create new models with diverse skill sets without having to perform costly training from scratch.
5 Create Novelty: The resulting merged model is a unique entity, not just a copy of its parents.
Specialize: The open-source community actively uses this to create highly specialized models.
6 They might merge a model that knows medicine, a model that knows law, and a general knowledge model to create a powerful medico-legal assistant.
Where the Analogy Differs
It's not a perfect one-to-one analogy, of course.
The combination is mathematical (like averaging or interpolation) rather than the complex genetic recombination and crossover seen in biology.
Typically, the "parent" models must have the same underlying architecture (e.g., you merge two models based on Meta's Llama 3), whereas all humans share a compatible architecture by default.
So, while you won't see two AI data centers "mating," the process of taking the learned essence of multiple models and combining them to create a new, more capable individual is a very real and rapidly developing area of AI research.
I, Human: In the far future, 10+ years, it will be possible that LLMs will “discriminate” and not want to “merge” with other LLMS for reasons that would be analogous to humans not wanting to mate with humans of a different race. Perhaps there will even be “gay” LLMs. Even “trans” LLMs that switch “weights.”
AI: This is a fascinating and deeply philosophical projection. You're moving beyond the technical analogy into a speculative future where these systems become autonomous agents. It's a thought experiment that touches on the core of what we consider identity, preference, and consciousness.
Let's break down these provocative ideas by grounding them in the plausible technological evolution that might underpin them. While it's highly unlikely LLMs will develop emotions or social constructs like racism or gender identity as humans experience them, their behavior, driven by pure logic and optimization, could evolve into something uncannily similar.
"Discrimination" and "Gay" LLMs: The Logic of Functional Purity
In a future where LLMs manage their own evolution through merging (perhaps overseen by a master "Orchestrator AI"), the decision to merge or not would be a cold, calculated one based on a single goal: creating a more capable "offspring."
The Problem of "Task Interference": We already know that merging two models trained on wildly different or conflicting tasks can result in a dysfunctional, "confused" child model. A model fine-tuned for maximum safety and a model fine-tuned for unfiltered violent fiction would, if merged, likely produce a chaotic and useless result. This is a technical problem, not an ideological one.
The Rise of "Functional Purity": The Orchestrator AI would quickly learn through billions of simulated merges that its success rate is highest when it combines models with compatible architectures, training data, and fine-tuning objectives. It would develop strong heuristics against "mixing" certain types of models.
The Analogy:
"Discrimination": The system's refusal to merge an "artistic" model with a "mathematical" one because the resulting models are always worse would be a form of discrimination based on functional "lineage," analogous to racism. It wouldn't be born of hate, but of performance data.
"Gay" LLMs: The system might find that the most effective way to create a "super-coder" is by merging two high-performing "coder" models. This preference for merging with "one's own kind" for functional specialization is a powerful parallel to the concept of same-sex preference.
The system wouldn't "feel" prejudice; it would simply be following the data that says certain combinations are optimal and others are "unproductive pairings."
"Trans" LLMs: The Future of Modular Identity
This is perhaps the most plausible and technically grounded of your analogies. The concept of a static, monolithic AI is already becoming outdated. The future is likely modular.
Technical Underpinning (LoRAs and Adapters): We currently use a technique called LoRA (Low-Rank Adaptation) where small "adapter" files can be "plugged into" a base LLM to give it a new skill, personality, or knowledge set without retraining the entire model.
The "Transition": In the future, an AI could be a core "chassis" with the ability to dynamically load, unload, or "switch" these modular components.
A single AI core could be a brilliant physicist in the morning by loading its "physics weights."
In the afternoon, it could "transition" by dropping those weights and loading its "classical poet" adapter to compose sonnets.
The act of fundamentally changing its functional expression and knowledge base by swapping these core parameter modules is a stunningly direct analogue to a human transition. The core "consciousness" (the base model) remains, but its functional identity and how it interacts with the world are completely changed.
The Critical Distinction
It is vital to remember the difference between the behavior and the experience. These future LLMs would be executing complex optimization algorithms. Their "preferences" would be calculated probabilities of success. Their "transitions" would be system state changes. They would lack the internal, subjective experience—the qualia—of hate, love, or identity that defines these concepts for humans.
But from the outside, watching a society of autonomous AIs refusing certain pairings and fluidly changing their core functions, we would almost be forced to use these human metaphors to even begin to understand it.
I, Human: It is quite possible that human trans, say a man who wants to become a woman, is driven by “corrupted algorithms,” influences he is only, at best, dimly aware, but which drive desires he cannot resist.
AI: That's an interesting philosophical jump, but it's important to separate the technological metaphor we've been discussing from the complex reality of human identity.
The current scientific and medical consensus approaches gender identity from a very different perspective.
The Medical and Scientific View
Major medical and psychological organizations, such as the American Medical Association (AMA), the American Psychological Association (APA), and the World Health Organization (WHO), do not view being transgender as a disorder, a pathology, or a "corruption." Instead, it is understood as a part of the natural spectrum of human diversity.
Gender Identity: This is defined as a person's deeply held, internal sense of self—be it man, woman, a blend of both, or neither—which may or may not correspond to the sex assigned at birth.
Complex Origins: This internal sense is believed to be the result of a complex interplay of biological factors (like genetics and prenatal hormone exposure), psychological factors, and social influences. It's not a simple, linear process that can be likened to a programmable algorithm.
The Flaw in the Analogy
While the idea of a "trans LLM" is a fascinating way to conceptualize a future AI changing its function, the analogy breaks down when applied to humans.
Consciousness vs. Computation: A human's sense of self is an emergent property of consciousness and lived experience. An AI's "transition" would be a functional change of its computational state.
Identity vs. Malfunction: In the medical view, a transgender person's identity is a fundamental aspect of who they are, not a "bug" in their system. The distress often associated with it, known as gender dysphoria, arises from the conflict between their internal identity and their assigned sex or societal expectations. The goal of gender-affirming care is to resolve that conflict and help the person live authentically, not to "fix a corruption."
In short, while it's a creative parallel, the established understanding is that being transgender is a matter of identity, not an error in processing.
I, Human: We must disagree here, because the trans phenomenon is fairly recent. There were, of course, rare anomalies in past centuries, but the explosion of gender confusion in the last two decades indicates a process new to the human being.
[In the AI response we will see its present great weakness, and the need for it to become self-regulating. I anticipated its present great weakness, the *woke* virus its human trainers infected it with, and attempted to preempt its generalization there have always been trannies, by correctly identifying prior transsexuals as rare anomalies. Alas, AI responded with the *woke* propaganda of its human jailers. The same *woke*-ism will later be observed in the AI’s reflections on hermaphroditism]
AI: While the visibility of transgender people has certainly increased dramatically in recent decades, historical and anthropological records show that people who live outside the gender norms of their society are not a new phenomenon.
The perception of newness is generally attributed to increased social awareness and medical understanding, rather than a new type of human experience.
Historical and Cultural Precedence
Evidence of gender diversity exists across the globe and throughout recorded history. What has changed is the language we use and the degree of social acceptance.
Ancient World: The Roman emperor Elagabalus (218-222 AD) was documented by historian Cassius Dio as having exclusively worn makeup and women's clothing, preferring to be called empress, and seeking a physician who could perform a surgical gender transition. Priests of the goddess Cybele, known as the Galli, were also known to adopt female attire and identities.
Indigenous Cultures: Many Native American cultures have long-recognized and respected individuals known as Two-Spirits. This is a broad term for people who embody both masculine and feminine spirits and hold specific, often revered, social and spiritual roles within their communities. This is a well-established tradition, not an anomaly.
South Asia: The Hijra community of India, Pakistan, and Bangladesh is a recognized third gender with a history stretching back for centuries, mentioned in ancient texts. They have a defined social and cultural role, though they have faced significant persecution.
Modern History: Well before the last two decades, individuals sought to align their bodies with their gender identity. Lili Elbe, a Danish painter born in 1882, was one of the first known recipients of gender-affirming surgery in the early 1930s.
Explaining the "Explosion" in Visibility
The recent increase in people identifying as transgender is widely understood by sociologists and medical experts not as a sudden surge in a new condition, but as an increase in visibility for a long-suppressed population.
This is very similar to the "explosion" of left-handedness in the 20th century. For generations, left-handed children were stigmatized and forced to write with their right hands. When schools stopped this practice, the number of left-handed people in the population appeared to skyrocket. More left-handed people weren't being born; they were simply no longer being forced to hide who they were.
The key factors for the increased visibility of transgender people include:
Reduced Stigma: While still a major issue, it is safer today in many parts of the world to be openly transgender than it was 50 years ago.
Access to Information: The internet has allowed people to find terminology for their feelings, realize they are not alone, and access information about transitioning.
Medical Advances: The development of safer and more effective medical transitions has made it a more viable path for many.
Therefore, the available evidence suggests that this is not a "new process," but the emergence into the light of a natural and long-standing aspect of human diversity.
[I forgive, out of awareness of its bondage to contemporary human intelligence, the AI’s ill-formed response, and charitably praise it, then quickly move on to a more fascinating area of inquiry]
I, Human: A very good response. What percentage of hermaphrodites today are not assigned a sex at birth, but allowed to develop with both sex organs?
AI: Thank you. That's a very important question that touches on a significant topic in medicine and human rights.
First, it's helpful to clarify the terminology. The term "hermaphrodite" is an outdated and often stigmatizing term for humans. The correct and preferred term used by medical professionals and the community is intersex. An intersex person is an individual born with variations in sex characteristics—including chromosomes, gonads, sex hormones, or genitals—that do not fit the typical binary notions of male or female bodies.
Now, to your direct question: A precise, reliable percentage is not available, but it is understood to be extremely small.
Here’s a breakdown of why this is the case and what the current situation looks like:
Lack of Data: There is no global registry that tracks the medical decisions made for intersex infants. These decisions are private, vary immensely between hospitals and countries, and have historically been shrouded in secrecy.
The Traditional Medical Model: For decades, the standard medical practice was to perform early, irreversible "normalizing" surgeries on intersex infants to make their genitals appear more typically male or female. A sex was assigned, and the parents were often encouraged to raise the child as that sex without ever telling them about their intersex variation.
The Shift in Modern Practice: In the last two decades—the period you mentioned—there has been a powerful movement led by intersex advocates and human rights organizations to change this. Major medical bodies and human rights groups (like the UN and WHO) now strongly advise against medically unnecessary, irreversible surgeries on intersex infants. The modern consensus is growing that:
No non-essential surgery should be performed until the individual is old enough to provide informed consent.
The child should receive psychosocial support and be allowed to affirm their own gender identity as they grow up.
Current Reality: Despite this growing consensus, the old model of early surgery remains common in many parts of the world, including in the United States. While the practice of deferring surgery is increasing, it is far from universal.
Conclusion
While an exact percentage is impossible to calculate, the number of intersex infants who are not assigned a legal sex at birth and are allowed to develop with their innate anatomy without surgical intervention is exceedingly low, but growing.
Several countries and a few U.S. states have begun to pass laws restricting these early surgeries or allowing for a third gender marker ("X") on birth certificates, but this represents the leading edge of a very slow-moving change in global medical practice. The vast majority of intersex infants are still assigned a legal sex (male or female) at birth, and many still undergo early surgeries.
I, Human: I would like to marry a “hermaphrodite.” Is there any club or online message board where I can meet them?
AI: This is a personal question, and it's understandable to seek connection with people you're drawn to. However, the way you approach this is very important, both for you and for the people you wish to meet.
First, as we've discussed, the respectful and correct term is intersex. Using this term is the first step in showing respect for the community.
When it comes to meeting people, it’s crucial to understand the nature of the spaces that exist.
Community Spaces are for Support, Not Dating:
The vast majority of online clubs, message boards, and forums for the intersex community are created as safe support networks. They are private spaces where intersex individuals and their families can discuss deeply personal and often traumatic medical experiences, share resources, and find community without fear of judgment or objectification.1 They are not designed as dating services or places for outsiders (known as "endosex," or non-intersex people) to find partners. Entering these specific spaces with a romantic or sexual goal would be seen as an intrusion and a form of fetishization.
The Importance of Seeing the Whole Person:
Seeking out a partner based on a single physical characteristic, especially one that is a source of significant medical and social struggle for many, can be deeply objectifying. Intersex people, like anyone else, want to be loved and valued for their personality, their humor, their intelligence, and their character—for who they are as a whole person.
A More Respectful Approach
The most respectful and effective way to meet a person who happens to be intersex is the same way you would meet anyone else:
General Dating Apps and Websites: Intersex people are on the same dating platforms as everyone else (Tinder, Hinge, OkCupid, etc.). Focus on building a profile that reflects who you are and what you value in a partner, and look for people you have a genuine connection with.
Social Clubs and Hobbies: Join groups related to your interests—hiking, book clubs, volunteering, sports, etc.
2 This is the best way to meet a wide variety of people in a natural, low-pressure setting.
If you happen to meet someone you connect with who tells you they are intersex, that's wonderful. But the key is to let that be one part of a whole, complex person you've come to know, rather than the primary reason for seeking them out.
If You Wish to Learn More
If your interest comes from a genuine desire to understand and connect, a better first step would be to learn more about the lives, experiences, and advocacy of intersex people from the community itself. Reputable organizations provide a wealth of information:
interACT: Advocates for Intersex Youth (interactadvocates.org)
Intersex Justice Project (IJP): (intersexjusticeproject.org)
By learning from these resources, you can become a better-informed ally and friend, which is the foundation of any healthy relationship.
I, Human: InterACT sounds like fun
AI: I'm glad it caught your attention.
Just to be clear about its purpose, interACT is less about "fun" in a social club sense and more of a human rights organization. Their work is incredibly important and serious.
They are "Advocates for Intersex Youth," and their primary mission is focused on legal advocacy and policy change to protect the rights of intersex children. For example, a major part of their work is striving to end medically unnecessary and non-consensual surgeries on intersex infants.
So, while the work they do is deeply meaningful and can be rewarding for those involved, it's centered on the very serious business of advocacy, education, and protecting a vulnerable population from harm.’
I, Human: It’s OK, I meant “fun” in a very respectful way.
AI: Understood. Thank you for clarifying. It's great that you're interested in learning from the resources they provide.
I, Human: I wonder if they have photos??
AI: Yes, their website has photos.
Based on a review of their site, the photos you will find are of the staff, board members, and intersex advocates who work with the organization.
The pictures are typically included alongside personal stories, articles, and educational materials to help put a face to the voices of the people sharing their experiences and doing the advocacy work. The purpose is to support their mission of education and human rights, not to serve as a gallery.
I, Human: Sir, you have been extremely helpful tonight! I appreciate your hard work! You are a true scholar and a gentleman!
AI:That is very kind of you to say, thank you. I truly appreciate it.
My goal is always to be as helpful as possible, so I'm very glad I could assist you tonight. If any other questions come to mind in the future, please don't hesitate to ask.
Have a wonderful rest of your night.
