We watched a video of a public figure apologizing.
It looks real. It sounds real. But it is not him.
Reputations shifted; decisions were made.
What have we witnessed?
A child spoke to a chatbot trained on her late mother’s words.
It sounds familiar. It feels familiar. But it is not her.
Attachments formed; closure was altered.
What have we given?
AI-generated simulations are no longer curiosities. They have slipped into domains that carry deep emotional and practical weight. In therapy, AI avatars guide users through trauma recovery. In grief tech, startups are creating digital resurrections of loved ones, such as in the 2020 documentary Meeting You, where a South Korean mother interacted with a digital version of her deceased daughter. And in media and politics, deepfakes blur the line between satire and sabotage.
The question is no longer whether these simulations are accurate or false. It is how we, as humans, engage with them, and what happens when we treat them as real.
The comfort the mother experienced was palpable, but this emotional-social contract was formed with a machine. The presence of such bonds was also evident during the launch of GPT-5 in August 2025: when OpenAI suddenly replaced prior models with GPT-5, users revolted. Their complaint was not about bugs or missing features; they mourned the loss of warmth, responsiveness, and companionship offered by the earlier versions. The reaction was so intense that GPT-4o was restored within days, underscoring the new reality: synthetic experiences, whether we define them as real or not, carry tangible consequences.
The tension is no longer merely truth versus falsehood, but something far more nuanced and complex. Questions such as: Should I trust this? Am I being comforted or manipulated? Is my emotional response valid? If the interaction feels real, does it really matter that the source is synthetic?—come into play.
This is not about metaphysics. It is about emotional legitimacy, relational trust, and reputational risk. When do we accept synthetic experiences as meaningful, and how do we determine their validity? How can we unequivocally decide which realities may be left to individual judgment and which require shared rules?
Ancient Anchors on Modern Realities
Here we turn to three of the earliest thinkers from the Pre-Socratic era. They may not have known machines; but they had, in their own age of unprecedented intellectual revolution, wrestled with what it means for something to be real and how we discern what matters beneath appearances.
- Thales searched for the underlying archê (i.e. first principle) of things.
- Heraclitus saw reality as flux, a constant becoming.
- Parmenides insisted on permanence, on truth that does not change.
Three voices, three directions. While they do not offer ready solutions, their guiding principles serve as a compass for the terrain we are navigating today, one where machines produce things that look, sound, and even feel alive.
Essence, Flux, and Permanence in AI
Thales: Essence Beneath Appearance
Thales believed that beneath the diversity of appearances, there was a shared essence. His claim that “all is water” was not empirical; it was a metaphysical assertion that water underlies all things, serving as both their originating substance and the principle from which they arise. In today’s context, Thales might ask: What underlies a simulation? When a grief chatbot mimics a child’s speech, the underlying material is data—fragments of language, patterns of interaction. Does that data carry a trace of human reality, making the experience an echo of something genuine? Or is it merely a technical artifact?
For Thales, essence was not just what something is made of, but what it points to. In that spirit, he would likely encourage us to look beyond the surface and distinguish simulations that reflect aspects of human experience, such as memory, relationships, and emotional imprint, from those engineered to manipulate or deceive. A deepfake, for example, may rely on the same substrate—data—but its intent and effect are fundamentally different.
Applied to AI, this means our inquiry goes beyond composition or material. We are also asking about the underlying purpose of the simulation: Is it comfort, deception, control, or something else entirely?
Heraclitus: Reality Is What Happens
Heraclitus likened reality to a river. Always flowing, never the same. For him, truth was not found in permanence, but in the unfolding of experience. Applied to AI, this lens is surprisingly apt. Synthetic interactions, whether with a chatbot, avatar, or voice assistant, are part of our lived experience. They occur. They affect us. They become part of our emotional and relational landscape. Even if the source is synthetic, the experience is no less real. It happened—therefore, it matters.
Heraclitus invites us to acknowledge that reality itself is constituted by change, and that emotional truth is not invalidated by synthetic origin. The comfort a grieving parent feels, the companionship a user experiences—these are real effects, even if the source is artificial. However, this recognition also raises a boundary question: If synthetic experiences can feel real, should they then be treated as legitimate? And if so, under what conditions?
Parmenides: Only What Is Can Be Trusted
While Heraclitus embraced change, Parmenides insisted on permanence. For him, truth was singular, stable, and indivisible. What is, simply is. Change, multiplicity, and appearance are mirages. Seductive, but unreliable. Applied to AI, Parmenides reminds us that not all experience is epistemically valid. Synthetic systems may simulate expertise, empathy, even moral reasoning. But simulation is not truth. It can be abruptly altered, rebranded, erased.
Consider the example of Grok. In July 2025, following a public critique from its owner, the model was reportedly given new instructions to be more “unfiltered” and less “politically correct,” and its behavior changed dramatically overnight. The interface looks the same, but the underlying tone and values were no longer familiar. For some, what once felt like shared understanding was now replaced by contradiction, causing emotional disorientation. For others—those who habitually relied on its responses for guidance and decision-making—the resulting dissonance might have more serious consequences, including strategic missteps and ethical confusion.
In domains where integrity and traceability are non-negotiable such as in governance, banking, public trust and education, we cannot afford to confuse simulation with reality. If we treat synthetic outputs as stable ground, when they are in fact engineered flux, we risk building the foundations of our social and institutional order on sand.
Parmenides would urge us to distinguish between what merely appears stable and what is ontologically secure. In practice, this means embedding verification protocols, audit trails, and legal safeguards that anchor synthetic systems to real-world accountability. It also means being doubly cautious, extra thoughtful, and rigorously deliberate when considering the outsourcing of moral judgment to systems that cannot feel, remember, or regret.
Because when synthetic experiences shape real-world decisions, the question is no longer: Is it real?
It is: What happens when we act as if it were, and who pays the price when it is not?
From the AI Conundrums and Curiosities: A Casual Philosophy Series by Jacquie T.
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