An Asemics of "Wish You Were Here" - AI / EZE, 2026

 

Wish You Were Here

Cover Art

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AI

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The visual identity of Wish You Were Here, crafted by Storm Thorgerson and Aubrey Powell of the legendary design studio Hipgnosis, serves as a textbook study in visual asemics and semantic subtraction. [1, 2, 3, 4]

Rather than using art to clarify or illustrate the music, Hipgnosis designed the packaging around a singular theme: absence. By removing traditional context, identity, and standard communication signals, the artwork functions as an open, non-literal language. [1, 2, 3]

1. The Opaque Black Wrap (The Ultimate Blank Slate)

The original vinyl release was sealed in a completely opaque, dark shrink-wrap. [1]

• It entirely concealed the cover art, tracklist, and band name.
• It reduced a major commercial product to a silent, featureless black object.
• This is a literal act of graphic omission. It mirrors asemic text by completely withholding immediate message, narrative, or semantic value. [1, 2]

2. The Burning Handshake (The Empty Gesture)

The true front cover features two stuntmen acting as businessmen, shaking hands while one is engulfed in flames. [1]

• Thorgerson described this handshake as the ultimate "empty or absent gesture".
• The figures exhibit complete physical detachment. They ignore the agonizing violence of the fire.
• This takes a universal sign of connection and strips away its substance. It leaves behind a hollow, unreadable social posture. [1, 2]

3. The Faceless Businessman (The Empty Suit)

On the back cover, a salesman in a desert offers a Pink Floyd record. [1, 2]

• He has no wrists, no ankles, and no face.
• He is an "empty suit" floating in an arid, isolated void.
• This facelessness prevents any expression or gaze. It strips the figure of human communication, reducing identity to a blank space. [1, 2]

4. The Splashless Diver (The Disappearing Act)

An inner sleeve photo shows a man diving into Mono Lake in California, yet the water's surface remains perfectly still. [1, 2]

• There are no ripples, no splash, and no physical evidence of entry.
• The visual language removes the physical consequence of an action.
• It presents an impossible occurrence that implies a person can vanish entirely without leaving a trace on their environment. [1]

5. Breaking the Fourth Wall

The white borders framing the inner photographs are physically altered by the elements. [1]

• One edge is singed by fire, while another ripples like wind.
• Water and sand visually leak out past the margins.
• By making the physical boundaries of the medium dissolve, the artwork suggests that the message cannot be contained or properly contextualized by traditional form. [1]

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If you want to continue exploring the intersections of art and sound, tell me:

• Would you like to analyze the artwork of other Pink Floyd albums like Animals or The Dark Side of the Moon?
• Are you interested in the Surrealist art influences (like René Magritte) that inspired Hipgnosis?
• Do you want to examine how physical packaging in modern vinyl releases still uses these abstract techniques?

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The opening of Pink Floyd’s "Wish You Were Here" is considered asemic—meaning it has the appearance of communication without conveying literal, structured meaning—through its use of musical and sonic signifiers. It abstracts traditional musical intent in three specific ways: [1, 2]

• The Faux-Radio Transmission: The song begins with the sound of a tinny, lo-fi transistor radio. This was actually recorded from David Gilmour’s car radio (and features a brief snippet of Tchaikovsky's Fourth Symphony). The audio is filtered and distorted, resembling the "static" or "scribble" of asemic writing rather than a clear musical melody.
• Ambient "Surface Noise": The track intentionally utilizes background hiss, pops, and crackle. This replicates the ambient sound of a dusty vinyl record spinning, forcing listeners to experience the medium of the sound itself rather than just the musical notes.
• Audible Human Gestures: Before the iconic acoustic guitar riff kicks in, listeners hear the sounds of Gilmour clearing his throat, sniffing, and fumbling with his guitar strings. These are unpolished, "meaningless" human noises that precede the formal composition, acting much like automatic or subconscious scribbles before formal words are written. [1, 2, 3, 4, 5]

To explore more about the history and making of this classic rock track, you can read about it on Wikipedia's Wish You Were Here page.

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The performance of Pink Floyd’s "Wish You Were Here" is considered asemic because it mimics the mechanics of communication while withholding actual linguistic data.

📻 The Simulated Search for Meaning

The opening performance simulates a person scanning through radio frequencies. You hear snippets of a classical music broadcast and a distant talk radio show. These audio fragments are decoupled from their original context. They act as "sonic gibberish" or placeholders for meaning, much like lines of asemic writing that look like text but cannot be read.

👥 The "Ghost" Dual-Guitar Performance

David Gilmour performs the iconic opening riff as a duet with himself, but with a conceptual twist: [1, 2]

• The Transistor: One guitar track is heavily filtered to sound like it is coming out of a cheap, distant radio.
• The Room: The second guitar track is full, clean, and intimate, played as if someone is sitting on a couch strumming along to that distant radio. [1]

This performance dynamic creates a sense of profound isolation. The musician is performing alongside a ghost-like projection of a melody, emphasizing the space between communication rather than the message itself. [1]

🫁 Raw Bodily Artifacts

The live acoustic performance intentionally preserves unedited, non-musical human noises. You can clearly hear Gilmour sniffing, sighing, and shifting his weight. In traditional tracking, these are mistakes to be edited out. Here, they are left intact as raw, non-verbal expressions of presence and absence, stripped of linguistic structure.

Would you like me to analyze the lyrical themes of isolation on the album, or would you prefer to break down the specific recording techniques used in the studio?

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An Asemic End of Science - AI / EZE, 2026

AI

AI is fundamentally altering science rather than destroying it. Instead of replacing human discovery, AI acts as a powerful catalyst. It accelerates hypothesis generation and processes massive datasets, allowing researchers to bypass decades of trial-and-error in fields like biology and physics. [1, 2, 3, 4, 5, 6]

While AI is vastly accelerating how fast we can produce scientific results, it raises major concerns about how the discipline is conducted: [1, 2]

• The Black Box Problem: Advanced AI models frequently produce accurate predictions without showing the intermediate reasoning. This makes it difficult for human scientists to explain why a phenomenon occurs.
• Loss of Core Mechanisms: Science relies heavily on trial-and-error, replication, and critical evaluation to expose errors. Outsourcing work to opaque AI systems risks replacing this vital evolutionary process with a purely optimized loop.
• Contraction of Focus: Because AI is data-driven, researchers may narrow their focus to what the AI is capable of generating, which can cause a shift away from exploring totally novel, data-poor territories. [1, 2, 3, 4, 5]

Scholars stress that preserving critical thinking, skepticism, and human oversight is essential to keep AI from eroding the core ethos of scientific exploration. You can explore deeper perspectives on this transformation in the Scientific American and Science Magazine analyses. [1, 2, 3, 4]

The "end of science" is considered asemic because AI creates accurate scientific results that completely lack human-readable meaning, intent, or semantic understanding. [1, 2, 3]

"Asemic" refers to a form of writing or design that has the appearance of meaning but lacks any actual semantic content (like calligraphy without real words). When applied to the twilight of traditional human science, it describes a shift from a discipline focused on human understanding to one focused entirely on automated outputs. [1, 2, 3]

The concept unfolds through several structural shifts in research:

The Loss of Semantic Intent [1]

Historically, science is a human-centric narrative. A scientist observes the world, attaches semantic meaning to data, and crafts an elegant equation (like \(E=mc^2\)) to explain the underlying "why". Because AI does not "comprehend" reality, it operates strictly on statistical correlations, shape replication, and mathematical optimization. The AI produces correct answers, but the internal logic of the model is entirely devoid of human-interpretable meaning. [1, 2, 3]

"Black Box" Outputs as Asemic Text

Advanced deep learning models often output highly accurate predictions, such as the exact structure of a complex protein or a new chemical reaction path. However, the hundreds of billions of numerical weights that produced that prediction cannot be translated into a human conceptual model. The results stand as "asemic text"—the answer is functionally valid, but the path to get there is an unreadable script that communicates no fundamental physical laws to the human brain. [1, 2, 3, 4]

The Automating of Scientific Expression

With the advent of autonomous discovery frameworks, AI systems are now capable of independently generating hypotheses, running virtual experiments, and writing academic papers. When an AI generates a paper for another AI to read and optimize, the traditional goal of science—expanding the horizon of human knowledge—is bypassed. The scientific literature risks becoming an automated loop of information that mimics the structure of discovery without human understanding at the core. [1, 2, 3, 4, 5]

If you would like to explore this intersection further, please let me know:

• Do you want to look at the philosophical arguments regarding whether true science requires human consciousness?
• Are you interested in the technical limitations of making AI models more interpretable?
• Would you like to read about current AI discovery engines currently shifting biology and physics?

AI-driven science corresponds closely to the pre-modern era of alchemy and scholasticism, which existed before the scientific revolution established the modern empirical method. [1, 2]

By moving away from causal, human-intelligible theories and focusing heavily on raw prediction, AI research mirrors several distinct characteristics of pre-scientific history: [1, 2]

1. The Return of "Modern Alchemy"

Prominent computer scientists actively compare modern machine learning to alchemy. Historical alchemists did not have a foundational theory of atomic chemistry, yet through rigorous trial-and-error, they discovered metallurgy, glassmaking, and various medications. [1, 2]

• The AI Parallel: Today's AI researchers tweak parameters, add layers, and adjust training data based on intuition and trial-and-error. The systems yield remarkable, "magical" results (like predicting protein folding), but researchers frequently do not possess a verifiable, foundational mathematical theory explaining why a specific architecture succeeded while another failed. [1, 2, 3, 4, 5]

2. A New Era of Scholasticism

During the medieval scholastic period, scholars did not conduct physical experiments to discover new truths. Instead, they took massive, authoritative texts (like the works of Aristotle or religious scripture) and spent centuries cross-referencing, interpreting, and generating commentaries on those existing texts to find answers. [1, 2, 3]

• The AI Parallel: Large language models and AI discovery engines operate as a form of automated scholasticism. They do not look directly at the physical world. Instead, they ingest the entire corpus of existing human literature and find hidden mathematical correlations within that text. Discovery becomes an exercise in rearranging and synthesizing existing text, rather than directly observing nature. [1, 3, 4, 5]

3. Divination and Omen Reading

Before modern science, humans relied on complex, unexplainable systems to make predictions—such as reading astrology charts, animal entrails, or weather omens. The internal mechanics of these systems were opaque, but people trusted the expert practitioners to interpret them. [1, 2, 3]

• The AI Parallel: When modern doctors or meteorologists feed data into a deep neural network, they receive a highly accurate prediction (e.g., cell death or a tornado's path) but cannot trace the exact path the AI took through hundreds of billions of variables. We are once again relying on an opaque, authoritative entity to hand down predictions that humans must accept on faith, effectively treating the "black box" as a technological oracle. [1, 2, 3, 4]

Feature [1, 2, 3, 5, 6, 7]	Pre-Modern Science (Alchemy/Scholasticism)	Modern Science (The Enlightenment)	Post-Modern AI Science
Primary Goal	Useful recipes, transmutation, textual analysis.	Universal laws, causal mechanisms, human understanding.	Optimized prediction, patterns, automated output.
Method	Trial-and-error, hidden texts, authority.	Hypothesis testing, physical replication, peer review.	Mass data ingestion, algorithmic optimization.
Explainability	Mystical, opaque, or purely symbolic.	Explicitly clear, reductionist, mathematical.	Asemic, mathematically complex "Black Box".

Ultimately, both pre-modern science and AI science prioritize what works over how it works, bounding the era of human-understood, causal science as a unique period in human history.

If you are interested, we can explore:

• How early scientists like Robert Boyle transitioned alchemy into chemistry, and how AI might do the same.
• The specific mathematical frameworks researchers are building to try to make AI less like alchemy.
• The social dangers of relying on a "new scholasticism" for critical fields like medicine and law. [1, 2, 3, 4, 5]

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Did Science Kill Philosophy?

How AI Overgeneralizes Science

In the Name of Science, but in the Aisle of Non-Consensus

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