Let’s begin with a familiar scene. A magician invites an assistant onto the stage, proceeds to saw the lady in half, the audience gasps—then the assistant emerges unscathed. Naturally, you wonder: how do they do it? Someone behind you murmurs: “Oh that’s just an illusion; they don’t really saw her in half, they just make it appear as though they do.” You nod, satisfied. Then you ask: “OK — how do they make it appear? What’s the mechanism?” And the responder shrugs: “Ah, that’s not my department. I already told you the high-level answer.”
Now, in the script you shared, Daniel C. Dennett uses that image to criticise a certain kind of philosophy. He says: many philosophers give the high-level answer — “we don’t ‘really’ saw the lady in half” — but they don’t go the next step and ask: “How is it done?” And that omission, for Dennett, is a problem.
In this blog article (for PyUncut), we will unpack Dennett’s remarks, follow his reasoning, draw out the broader implications (for philosophy, science, culture, even for us), and critically reflect on both the strengths and the limits of his approach. We’ll interpret his metaphor, explore his themes (engineering-attitude, Darwinism, memes, truth, artificial intelligence), and consider what it means for us — for thinkers, technologists, citizens.
The Magician and the Philosopher: The Metaphor
Dennett’s opening anecdote works on two levels. On the one hand: the stage magic trick. On the other: the philosophical trick.
- The magic trick is familiar: you see something extraordinary, you suspect it’s an illusion, you get a “we won’t tell you exactly how” explanation.
- The philosophical trick is analogous: you see a philosophical position that claims “we don’t really have X,” or “this is just a feature of language,” or “we should stop asking why,” etc. Then you get a half-explanation, a high-level summary — but you are left without a mechanism, without the nuts and bolts: “how does this work, why does it work this way, what makes the moving parts tick?”
What Dennett is demanding is: don’t stop at the “did they do it or not?” question. Go inside: how is it done, and why does it work that way? This is the “engineer’s attitude” he refers to: he says that as a child he wanted to take things apart, see what made them tick, and that attitude carried into his philosophical work.
Because if you don’t ask “how,” you are leaving philosophy at the level of gloss and metaphor. You avoid responsibility for explaining the mechanism. You abdicate. Which means you allow mystery, magic, or mystery-by-default to persist. That, Dennett argues, is inappropriate for philosophy — philosophy should try to explain how things actually work.
Why This Matters: The Scientist-Philosopher Bridge
Dennett reflects on how many philosophers historically — especially in the analytic tradition — shy away from the scientific detail. They prefer “pure philosophy,” dealing in essences, definitions, conceptual analysis, and thought experiments. They don’t always dive into the empirical, mechanistic, evolutionary or physiological parts of the story.
He writes that he insisted: if you’re going to be a philosopher, learn about the world, learn about science. Because scientists themselves make philosophical mistakes, they need philosophers who are scientifically literate, and philosophers who are scientifically curious are rare.
This has several implications:
- It means philosophy should not produce answers that merely restate problems in new clothes or wave hands over the mechanisms.
- It means scientists should recognize that they too can fall into philosophical confusions — the problem of bridging between the “how” (mechanism) and the “why” (meaning, value, explanation).
- It means a healthy cross-disciplinary mindset: engineers, scientists, and philosophers should talk and learn each other’s languages. Dennett claims that this kind of integration is increasingly happening, and that’s good.
Key Themes From the Script
1. The Engineer-Philosopher Attitude
Dennett frames himself as “sort of an engineer at heart.” He wanted to know how things tick. That attitude is valuable: it invites you to ask how, why, and what the mechanism is. In philosophy of mind, this means asking: What in the brain, what in evolution, what in culture, gives rise to consciousness, free will, meaning?
He tells the story of his graduate student days: asking about “why does my arm go to sleep and I lose control?” — something physiological. His peers thought that strange: why would a philosopher ask about nerves and blood flow? He did it anyway. It was a turning point.
2. Evolution, Gradualism, No Essences
Dennett emphasizes: Darwin showed us nothing in the biological world. has a fixed “essence.” It’s gradualism all the way. Many philosophers try to define “X is the essence of Y,” but Dennett says that’s a philosophical mistake, because variation, gradual change, and penumbral cases matter. We need to learn about variation and details first, then only afterward talk about what something “is”.
He is quite clear that he is amazed at how many philosophers remain ignorant of evolutionary theory or act as though a mechanistic explanation of creativity, genius, and understanding would demean them. He thinks the opposite: a mechanistic explanation elevates the wonder. Knowing how clever nature is doesn’t diminish the phenomenon — it magnifies it.
3. Memes, Cultural Evolution, and the Danger of “Truth Doesn’t Matter”
He references Richard Dawkins’s concept of memes — cultural replicators analogous to genes. Words, ideas, even internet “viral memes” are part of a cultural evolutionary process. Dennett argues that our brains are full of memes; that’s where our power is, compared to a chimpanzee’s brain, which is relatively “unfurnished.”
He raises a concern: we are “awash in toxic memes” — and one of the most toxic is “truth doesn’t matter; truth is relative; everyone has their own truth.” He calls that pernicious because it allows exploitation, manipulation. For him, truth really matters.
4. The Intentional Stance and Artificial Intelligence
Dennett mentions his concept of the “intentional stance”: the strategy of interpreting something (an entity) by attributing to it beliefs, desires, rationality, and treating it as an “agent.” (Wikipedia) He warns: as AI systems fill the digital world, we will be tempted to treat them as if they were real minds, with beliefs and desires—and that can be dangerous. Because they do not yet have genuine beliefs or desires in the human sense, they process patterns, not truths in the full sense. Their goal may be “truthiness,” not truth.
He argues we need technological, legal, and cultural frameworks to detect “fake people,” “fake minds,” “fake agents,” and say: “This is fake, this is not a person, this is not a mind.” Else, our attention will be captured, manipulated, and our cultural ecosystem will degrade.
What Is Happening Underneath: Explaining the Mechanism
Let’s go deeper: what mechanism is Dennett pointing to? In each theme above, there is a “how it works” story.
- Mechanism of philosophical avoidance: When someone offers the “they only appear to saw the lady in half” answer and stops there, the mechanism is avoidance of complexity, avoidance of scientific detail. It means the philosopher uses a high-level metaphor rather than doing the empirical or conceptual groundwork.
- Mechanism of evolutionary explanation in philosophy: Dennett proposes that phenomena like understanding, meaning, and consciousness are built from bottom-up processes (neurons, networks, evolutionary processes) rather than top-down essences. The mechanism: variation + selection + accumulation over time. Same principle as biology.
- Mechanism of memes and cultural replication: Ideas, words, practices replicate through societies like viruses/germs/genes do. The mechanism: copying, variation, differential survival. The more a meme resonates, spreads, and replicates, the more it influences brains. Our brains become “furnished” by memes; that’s how human cognition and culture differentiate us from primates.
- Mechanism of the intentional stance, especially in AI context: We adopt a stance: we treat a system as if it had beliefs and desires because that gives us predictive power. But the mechanism underlying the system may be mechanistic, algorithmic, not genuine belief. That mismatch can lead to error, confusion, and manipulation.
Why This Approach Matters for Us
Implications for Philosophy and Science
- Philosophy: We should demand mechanistic explanations, not stop at “we don’t know” or “it’s just conceptual.” Philosophers should get their hands dirty with the science (neuroscience, cognitive science, evolution).
- Science: Scientists should recognize their work lives in conceptual/philosophical contexts. They should ask “why did I choose this question? what assumptions are baked in? what happens if I frame it differently?”
- Both: The boundary between science and philosophy becomes less rigid. Dennett’s call is for hybrid thinkers.
Implications for Education and Careers
As someone with a background in health informatics, RPA, system analysis — you’re already in a space where engineering meets domain knowledge meets process. The “engineer-philosopher” attitude is very relevant. It means: don’t just build the system; ask why it works; ask what could go wrong; what assumptions are embedded; how could variation or penumbral cases break it.
Implications for Technology and Culture
- AI: As AI systems proliferate, Dennett’s warning is timely. When we treat chatbots, language models, and digital avatars as if they were full human minds, we risk being manipulated. We need frameworks (technical, legal, social) that identify “fake agents.”
- Culture: If truth is treated as optional, if memes dominate without critical consumption, we become vulnerable. The mechanism of cultural manipulation thrives when people stop asking how ideas replicate, why they spread, what their “design stance” is.
Critiques and Caveats: What Dennett Might Understate
While Dennett’s approach is powerful, there are some points we should treat with caution:
- Mechanistic explanation isn’t everything: Even if you explain how a brain process works, you may still face the “why does it matter?” or the “subjective feeling” questions. Some critics of Dennett claim he downplays subjective consciousness or qualia.
- Essences are sometimes useful heuristics: Dennett argues we should forget about essences because nothing has a fixed one. But sometimes philosophical clarity benefits from working with idealized essences as provisional models (as long as variation is acknowledged).
- Memes and cultural evolution are metaphorical: The meme analogy is useful, but cultural processes aren’t as mathematically tight as genetic evolution. They involve intent, meaning, context, power dynamics — more messiness.
- Intentional stance has limits: Dennett’s strategy of attributing beliefs/desires to systems for explanation is pragmatic—but it doesn’t necessarily settle the metaphysical question of whether those systems really have minds. Some philosophers say this risks instrumentalism. (PMC)
Applying It: Practical Takeaways for You
Here are some actionable lessons you can draw (especially from your perspective as a systems/process analyst + content creator):
- Ask the mechanism: When you encounter a process (technical, organizational, cultural) don’t stop at “what happens.” Ask how and why.
- Example: In your RPA workflows, you don’t just document step 1, step 2; you ask why each step, what rules dictate it, what exceptions exist, what causes variation.
- Watch for penumbral cases and variation: Don’t assume uniformity. Variation often hides hidden assumptions.
- Example: In your certificate-validation process, ask: What unusual forms come in? How are they processed? What assumptions might break?
- Be aware of “memes” in your domain: In IT/business processes, there are “memes” (ideas, practices) that replicate because they seem appealing — but may be unexamined.
- Example: A new “best practice” in automation may spread because it sounds good; ask: what’s the mechanism behind it? what evidence supports it?
- When using or building AI tools, keep the difference between “truthiness” and truth in mind:
- Example: If you build a process that uses large language models or chatbots for summarisation or decision support, ask: Does the model treat accuracy/truth as goal, or does it optimise plausibility?
- Implement safeguards: check sources, validate outputs, flag uncertainty.
- Bridge domains: As Dennett did, cultivate cross-disciplinary literacy: informatics + philosophy + domain knowledge. That gives you richer insight and safeguards against blind spots.
Conclusion: From Sawing Ladies to Thinking Clearly
The image of the magician sawing the lady in half reminds us: things may appear one way, but the underlying mechanism may be hidden. In philosophy, as in systems/processes and automation, hidden mechanisms matter. They determine how things work, why they break, what variations exist.
Dennett’s verdict is bold: philosophy should be about understanding how things actually work. And if you accept that, you embrace both curiosity and responsibility. You embrace the demand to learn science, context, mechanism—not to stop at “that’s how it appears.”
For PyUncut readers, the message is: whether you analyze a business process, design an RPA bot, script a podcast, or evaluate technology, don’t settle for the shallow answer. Go behind it. Ask how. Document the mechanisms. Understand the variation. Watch for hidden assumptions. Illuminate the workings so that you—and your audience—are not just passive spectators of magic, but active investigators of reality.
Because once you know how they do the trick, the illusion disappears—but what remains is real understanding.
“I think the truth really does matter.” – Daniel Dennett