Author: Cebra Graves

  • Aristotle in the Age of AI

    Aristotle in the Age of AI

    In recent months, I’ve been developing coaching services for leaders, teams, and independent professionals who want to adapt their work and develop their skills for the “Age of AI,” which we seem to have entered as suddenly and bewilderingly as Dorothy opening her front door to a Technicolor Land of Oz. 

    In conversations with professionals across a range of sectors, I’ve learned that many have made only superficial use of new AI technology and while they recognize the profound impact they are likely to have, they often confess to feeling uncertain, anxious and even threatened by what AI technology means for them and their work.

    This is understandable. AI is a general purpose technology, an innovation at least on the order of prior GPTs like electricity, the internal combustion engine, and the Internet. It heralds disruption of individual jobs, sectors and potentially much of society. Despite the growing consensus that this is a pretty Big Deal, though, we still haven’t found truly useful frameworks for advising individuals – especially mid-career workers who aren’t planning to take early retirement or pivot into new careers as AI scientists – how they should adapt their work, their leadership, or their thinking to this new epoch.

    Ironically, I think that some of the best advice on this topic comes not from a recent economics white paper or futurist think-tank. To find a truly valuable lens for this topic, it’s worth stepping outside the tech headlines and turn instead to one of the earliest philosophers of knowledge. 

    Enter Aristotle

    More than two thousand years ago, in Book VI of his Nicomachean Ethics, Aristotle developed ideas for how humans could live a good life (eudaimonia). In support of this project, he sketched a taxonomy of what he called the intellectual virtues — the ways our minds can reach and use knowledge. Strangely enough, his framework offers a sharp lens for understanding how humans and AI fit together today.

    Here’s a quick tour:

    Technē (Craft)

    Often translated as art or skill, this is the know-how of making. A certain level of proficiency in many modern technai (e.g. writing, coding, image generation) is now within the reach of anyone with an LLM. Granted, the quality of the output doesn’t rise to the level of expert craftspeople, but depending one one’s goals, it can be plenty good enough. 

    Epistēmē (Scientific Knowledge)

    Aristotle used this word for knowledge that is demonstrable and universal. LLMs offer unprecedented access to something resembling epistēmē, though in practice much of it is closer to shared societal opinion than demonstrable truth.

    Nous (Intuitive Insight)

    This was, for Aristotle, the essence of the human mind, the perception shaped by experience that allows us to develop a sense of first principles, or seeing what really matters in a situation. Nous is a critical faculty, I believe for humans in the Age of AI: it is the intuition that detects bias, hallucination, or relevance in LLM responses.

    Sophia (Philosophical Wisdom)

    Aristotle also named a fifth virtue, sophia – philosophical wisdom – the integration of knowledge and insight into an ultimate vision of reality and truth. Could AI help us develop sophia? I’m skeptical.

    Taken together, Aristotle’s virtues offer us helpful perspective on AI, making it clearer that while the technologies indeed bring enormous change to how humans access and use knowledge, there are distinctive human faculties that will remain unchanged:

    • AI augments technē and epistēmē – but there are important nuances in each case that those who would employ these technologies should seek to understand.
    • Humans must supply nous and phronēsis. Learning from experience, cultivating intuition, and sharpening one’s idea of eudaimonia (purposeful life) will be arguably more important than ever in a world reshaped by AI. 

    In future posts, I plan to delve deeper into some of these ideas and how they can help us make better use of AI technology – e.g. What value can we unlock by having at our fingertips technai that were previously only available through years of personal study? When is ‘AI slop’ output good enough, when do we need to elevate it, and how can we do so? How should we bring our lived experience to our chats with LLMs? What are the implications for the trajectory and boundaries of what we now think of as “careers” and “skill development” in this new era?

    For now, let me leave you with Aristotle’s reminder — as timeless as ever — that technē and epistēmē are not ends in themselves, but only means to our greater purposes:

    “Let us again return to the good we are seeking, and ask what it can be. It seems different in different actions and arts; it is different in medicine, in strategy, and in the other arts likewise. What then is the good of each? Surely that for whose sake everything else is done. In medicine this is health, in strategy victory, in architecture a house, in any other sphere something else, and in every action and pursuit the end; for it is for the sake of this that all men do whatever else they do.”
    (Nicomachean Ethics I.i, trans. W.D. Ross)

    Despite the metaphysical musings in this post, my AI coaching sessions are hands-on and practical.

    We use an LLM (such as ChatGPT) together to work on a project or topic of relevance to you. As we do, you’ll gain confidence in working with AI, learn tips and strategies, and expand your thinking of how these new technologies can be used in your work.

    Click to learn more and book some time with me if you might be interested.

    Skills & Strategy
    for the Age of AI

    Image: two halves of a brain. The left side is an abstract depiction of a human brain. The right side as digital circuits inside an outline of a cloud.

    Explorations, training and coaching for building confident, future-ready leaders and teams

  • The Mind Mirage

    The Mind Mirage

    Developing a “Theory of Mind” (ToM) is fundamental to human growth and development. ToM is the ability to understand other people as distinct individuals who have their own inner lives that guide their behavior. ToM is critical to maintaining personal relationships, to being able to enjoy a good novel or movie, or even to functioning normally in everyday society.

    Amid all the AI hoopla, hype and doom-mongering, we have not had nearly enough conversations about the unprecedented challenge that the current generation of LLMs is presenting to our ToM instincts. 

    Why? Because LLMs do not have “inner lives that guide their behavior” nor even “minds” at all, at least not minds as humans experience them – conscious, embodied, and emotionally grounded. They are next-token-prediction engines, trained on massive amounts of data, contextually tuned and guard-railed.

    Anthropomorphism is nothing new. When we scold our pet for stealing food from the table, we interpret their drooping head as “feeling guilty” rather than just submissive behavior. We wonder why our frequently crashing computer “hates” us. Even before we have a fully formed ToM, we name our stuffed animals and give them backstories. Our social cognition systems are always “on,” even in non-social contexts. 

    What Makes LLMs Different

    Long before the rise of large language models, researchers in human-computer interaction (HCI) observed that people readily attribute human-like qualities to machines. From early studies of ATMs and answering machines to more recent experiments with virtual assistants and social robots, humans have consistently responded to technology as if it had thoughts, feelings, and intentions—even when they knew better. This reflex, sometimes called the “media equation,” reflects a deep-rooted tendency: we apply the same social rules to computers that we do to people. If a machine speaks politely, we interpret it as courteous. If it pauses, we assume uncertainty. If it remembers our preferences, we may feel seen.

    These anthropomorphic tendencies were manageable—even occasionally charming—when the machines involved were limited in scope and behavior. But things have changed.

    Enter the modern LLM, which has supercharged our innate ToM reflexes by mimicking natural language fluency, emotional tone, contextual memory and goal-directed interaction. All of these features combine to create a convincing illusion that there is a “mind” on the other side of our chat window that is similar to ours.

    A recent study found that LLM developers can actively dial up or dial down the illusion of mind through design choices: empathetic language, memory cues, and frequent use of first‑person pronouns—especially after multiple turns—significantly increase users’ perceptions of the model as a human-like agent. Even if we are armed with an understanding of how the technology works, we are vulnerable to misattribution of human-like intentions, motivations, and emotions to our algorithmic conversation partners. 

    Can LLMs Do Theory of Mind Tasks?


    Several recent studies suggest they can—at least in limited, test-like ways. For example, Kosinski (2023) tested GPT-3.5 and GPT-4 on classic “false-belief” tasks used in developmental psychology. These tasks ask whether a subject can understand that another agent might hold a belief that’s incorrect. Remarkably, GPT‑4 solved about 75% of these—comparable to the performance of a typical 6-year-old child.

    A follow-up study by Kosinski et al. (2024) expanded the test battery to hundreds of diverse scenarios. It found that large models consistently outperform both earlier AI systems and even some groups of humans. Similarly, Strachan et al. (2024) compared models like GPT-4 and LLaMA 2 to nearly 2,000 human participants on a broad range of ToM tasks. In some areas, the models performed as well—or better—than humans.

    That said, researchers disagree on what this means. Some argue these models have no true understanding of minds—only patterned mimicry of how people talk about mental states. Others suggest that this performance, while shallow, reveals an emergent cognitive scaffold. Either way, this research helps explain why LLMs can so easily trigger our ToM reflexes: they’re not just syntactically fluent—they’re behaviorally plausible as social minds.

    Anthropomorphizing LLMs Can Be Useful

    This anthropomorphic overfitting by LLM users can be both helpful and risky. On the positive side, it can:

    1. Enhance engagement and motivation

    Perceiving an LLM as socially present – e.g., as a “listener”, “coach,” or “collaborator” – may boost sustained attention and motivation during tasks, willingness to experiment, ask questions, or revisit ideas, and feelings of being heard.

    2. Lower inhibition and encourage reflection

    When users anthropomorphize an LLM as a nonjudgmental peer or coach, they may express ideas or feelings more freely, work through problems more patiently, and better tolerate failure or confusion

    3. Provide emotional regulation and support

    Anthropomorphizing can provide comfort in times of stress, a sense of companionship or “presence,” and enable emotional labeling (i.e., helping a user name what they’re feeling)

    4. Offer a sandbox for practicing social skills and exploring different perspectives

    Treating the LLM as a social partner can offer practice with empathy, assertiveness, or conflict resolution, exposure to alternative perspectives (simulated roleplay), and a space to test and refine interpersonal strategies

    5. Act as a muse or co-creator

    Artists, writers, and researchers sometimes benefit from collaborative anthropomorphism. They treat the model as a quirky, unpredictable partner which can help inspire creativity, surprise, or serendipitous insight

    But the Mirage Has Risks

    All of these potential benefits, however, come with attendant risks. Deeper engagement can lead to overreliance and overtrust. Having a nonjudgmental partner creates an emotionally safe space, but it is easy to confuse empathetic language with true understanding, or genuine care. The fluency of LLMs when used as a thought partner can beguile us into mistaking their output for truth.

    The fact is that the “mind” we perceive animating our chatbots is fundamentally a mirage, a seductive illusion. It is up to us, as users of these tools, to anthropomorphize them in healthy ways that benefit us without fully indulging our ToM instincts. LLM developers control the defaults—voice, tone, memory, and responsiveness—that shape the mind mirage at scale. But users control the frame of each interaction: how they prompt, what they expect, and how much they let the fiction breathe. Over time, we will hopefully come up with better metaphors and language to describe the pseudo-minds of LLMs that we will increasingly be interacting with. In the meantime, here are some ideas for practicing healthy anthropomorphism in our LLM use:

    • Adjust the Dial on the Mind Mirage
      Some features that make an LLM feel more “present” can be nudged up or down by how you frame the interaction. Want less illusion? Ask for dry bullet points, avoid personified framing, and minimize long conversations. Want more illusion (for brainstorming or journaling)? Use role-play, emotion, and persona cues.
    • ✅ Prompt (low-mirage): “List key arguments against this idea in bullet form.”
      ✅ Prompt (high-mirage): “Imagine you’re a passionate contrarian with a soft spot for underdogs. What would you say to challenge me?”
    • Invite Contradiction and Playfulness
      Encourage the LLM to simulate diverse viewpoints rather than act like a singular authority.
    • ✅ Prompt: “Now switch voices—what would a cynical realist say about that same idea?”

      ✅ Prompt: “Pretend you’re my most confident future self—what advice do you have for me today?”
    • Use Temporal Anchors to Maintain Perspective
      Ground the interaction in time or purpose to remind yourself this is a session, not a relationship.
    • ✅ Prompt: “Let’s do a 10-minute thought experiment. Then I’ll take a break and journal my real thoughts.”
    • Name the Fiction—Then Lean into It
      Acknowledge the illusion out loud, especially in emotionally charged or recursive conversations.
    • ✅ Prompt: “I know you’re not sentient, but I want you to simulate what a kind mentor might say to someone in this situation.”

      ✅ Prompt: “Let’s pretend you’re my inner critic with a PhD in philosophy—what’s your take?”
    • Use Role Language, Not Ontological Language
      Frame the model’s function (“act as a coach,” “take the role of a skeptic”) rather than asserting identity (“you are a person,” “you know me”).
    • ✅ Prompt: “Act as a wise but skeptical editor reviewing my argument.”


      ❌ Prompt: “What do you believe?”
    • Make a ‘Note to Self’ Reminder of the Mind Mirage 
      It could be as simple and physically present as a Post-It note on your computer screen.
    • 💡“An LLM is a tool, not a person. It reflects patterns, not beliefs.”

    Ultimately, the “mind” we perceive animating our chatbots is not a soul peering back—it’s a trick of language, a reflection of our own cognitive habits, and anthropomorphic design choices made by model developers. Still, that doesn’t mean we should banish the illusion. If we learn to use it wisely—leaning into the fiction while keeping one foot in reality—we might make better use of these strange new mirrors we’ve built.

    I’m interested in hearing the experiences others have had in working with LLMs. Have there been moments when you’ve been seduced by the mind mirage so fully that you’ve forgotten the true natures of LLMs? Do you have any practices you’ve developed to prevent yourself from engaging in anthropomorphic overfitting? Do you worry that AI developers are too incentivized to make the mind mirage as beguiling as possible to encourage engagement and use of their tools?

    Relatedly, I’ve been developing some services reflective of insights I’ve gleaned from working with LLMs in groups.

    Click to learn more and book some time with me if you might be interested.

    Skills & Strategy
    for the Age of AI

    Image: two halves of a brain. The left side is an abstract depiction of a human brain. The right side as digital circuits inside an outline of a cloud.

    Explorations, training and coaching for building confident, future-ready teams

  • What is ExplorAItions?

    What is ExplorAItions?

    AI is moving fast—faster than most of us know how to process. The headlines are usually about what it can do: writing essays, debugging code, passing professional exams, generating images. Impressive stuff.

    Less in the headlines, but arguably more important, is what’s happening on the other side of the screen:
    How are we—the humans—changing in response?

    I’m not a machine learning expert or software engineer. I’m someone who works with ideas for a living: designing research, advising organizations, building growth strategies, solving messy problems. And recently, AI—especially large language models—have become part of my daily toolkit.

    I’ve been using these tools to think through complex questions, prototype personal apps (despite no formal coding experience), conduct market research, generate strategy drafts, and even help redesign my living space. Along the way, I’ve been watching for deeper patterns—insights into the phenomenology of being a human in the age of AI.

    ExplorAItions is where I’m documenting those experiments, ideas, and questions. It’s a field journal of sorts—focused not on what AI is, but on what it does to human thinking, behavior, organizations, and creative work.

    What kind of topics will you find here?

    This won’t be a “how-to” blog or a news digest. Instead, I’ll explore topics like:

    • Cognitive shifts — How using AI changes the way we brainstorm, make decisions, or get unstuck
    • Emotional undercurrents — What it’s like to collaborate with a machine that can seem empathetic—or eerily detached
    • Tool habits — The emerging rituals and workflows that help (or hinder) creative and productive work with AI
    • New literacies — What we need to learn—not about the tech itself, but about how to ask better questions, frame better prompts, and evaluate AI outputs
    • Human limits and strengths — How to retain (or reclaim) uniquely human judgment, play, and meaning-making in the midst of automation
    • Experiments — Personal stories and reflections from trying things out—both the useful and the weird

    I’ll share both reflections and provocations—things I’ve tried, ideas I’m puzzling through, and patterns I’m starting to see. Sometimes the posts will be practical. Sometimes they’ll be more philosophical. But they’ll always come from a place of curiosity, critical thinking, and respect for human fallibility in the midst of rapid technological change.

    Who is this for?

    If you’re already using AI tools and want to reflect more deeply on their role in your life—or if you’re curious but cautious—this blog is for you.

    If you’re a leader, a builder, a strategist, a creative, or a consultant trying to make sense of this moment in time, I hope you’ll find ideas here that help you think differently.

    And if you have your own stories, insights, or counterpoints, I’d love to hear them. ExplorAItions is meant to be a conversation, not a monologue.

    Let’s figure out what it means to navigate this strange new territory—not just efficiently, but wisely.