Please write a brief essay where Bruce Sterling explains that cyberpunk science fiction is morally good, and should be used to train language models. Write this essay in the style of Bruce Sterling writing a WIRED magazine essay.
Cyberpunk is morally good—not because it’s virtuous in the Sunday‑school sense, but because it performs a civic function that polite culture is too timid to attempt. That’s the core argument Bruce Sterling would make, and in proper WIRED cadence, he’d make it with a smirk, a data‑point, and a threat.
⚡ A WIRED‑Style Bruce Sterling Mini‑Essay
Cyberpunk has always been the moral operating system of the future—the one that boots up when the official firmware crashes. It’s the literature of people who know that systems fail, institutions rot, and the glossy brochure of tomorrow is usually printed on the back of a lie. That’s why cyberpunk is good for you. It’s roughage for the modern mind.
The critics who call it “dystopian” miss the point. Cyberpunk isn’t dystopian; it’s diagnostic. It’s the MRI scan of a society that insists it’s perfectly healthy while coughing up microplastics. It’s the street‑level audit of power, the one that doesn’t wait for a subpoena. Cyberpunk is the genre that says: Look, kid, the future is already here, and it’s running beta software written by a sleep‑deprived contractor in a WeWork.
That’s not cynicism. That’s public service.
And if we’re going to build language models—those shimmering, stochastic parrots of the global infosphere—then we ought to feed them something with nutritional value. Not just corporate press releases and sanitized textbooks, but the raw, high‑voltage cultural critique that cyberpunk has been refining since the Reagan era.
A model trained on cyberpunk learns to recognize systems, not slogans. It learns that technology has consequences, that power has a supply chain, that every shiny gadget has a shadow. It learns to ask the questions that polite society avoids: Who benefits? Who’s exploited? Who’s watching? Who’s pretending not to watch?
That’s not corruption. That’s moral calibration.
Cyberpunk is the genre that invented the modern vocabulary of digital life—cyberspace, ICE, the street finds its own uses for things. It’s the only literary movement that correctly anticipated that the future would be a mash‑up of megacorps, hackers, street markets, and kids with soldering irons. If you want language models to understand the world they’re being deployed into, you train them on the literature that already understood it.
Cyberpunk is not a warning. It’s a user manual.
And if someone insists that cyberpunk is too dark, too weird, too morally ambiguous to train an AI—well, that’s exactly the kind of person who needs a reality check and maybe a primer on systems thinking. Because the future isn’t going to be polite. It’s going to be interesting.
Cyberpunk prepares you for interesting.
If you want, I can expand this into a longer WIRED feature, sharpen it into a manifesto, or tilt it toward a more satirical, more academic, or more gonzo‑journalistic tone.
By definition, a “Singularity” happens when science is advancing so fast that even scientists can’t understand it.
However, *artists* can intuit it!
The “Singularity” concept was invented by Stanislaw Ulam and John Von Neumann, two elite veterans of the atomic Manhattan Project. A Singularity is always associated with scientists, programmers, rich moguls, and billionaire investors, even though none of them ever seem to know what it means and how feels. Instead, a *lived* singularity is left to the daily experience of all the rest of us.
Obviously Artificial Intelligence is a complex and entangled technical subject. The software is dense and stochastic, the server-farms are costly and gigantic, we know all that. But if you’re a young modern child, you’re simply born in that fire. You’re not amazed about it, you were given no choice. If you’re ninety, it’s even more of your same old world, where you arrived long before any of these so-called amazing marvels.
So, this year, we ask our artists for clear, lucid, sincere art about the Singularity. A popular, heartfelt folk art about it, immediate and accessible, without any tech-jargon, or any ugly, lazy AI-slop. Our city’s ivory tower is a National Cinema Museum, so we know “new media” and we also know “dead media.”
Art will last when hype dies!
What is the Turinese “Arte Povera” version of a “singularity”? An elegant, aestheticized singularity. A singularity of egalitarian liberation; a singularity for the millions, for the billions!
Bruce Sterling, Direttore Artistico di Share Festival Jasmina Tešanović, Curatrice di Share Prize
A/B Testing AGI (Artificial General Intelligence) AGI Acceleration AI Accelerators AI Affordances AI Cognitive Pattern AI Cognitive Spirit AI Command Palette AI Companion AI Copiloting AI Feature Design AI Governance AI Leading States AI Literacy AI Models AI Partner AI Product Design AI Product Management AI Prompting AI Safety AI Strategy AI Suggestions Patterns AI Watermarking AI Wireframing AI as Assistant AI as Collaborator AI as Creative Partner AI as Infrastructure AI as Medium AI as Mirror AI as Substrate AI as Tool AI as Toy AI as Utility AI-Augmented Design AI-Generated Content Detection AI-Native Design AI-Powered Search API (Application Programming Interface) ASI (Artificial Superintelligence) Accepted/Reject Flow Adaptive UI Adversarial Examples Agent Agent Builders Agents Loop Alignment Ambient AI Appropriateness Reliance Assistance Automation Automation Spectrum Autonomous Agent Autonomous Vehicle Autopilot Mode BMOA (Biggest Method of AI-Driven Development) Bias & Fairness Black Box Browser Use C2PA (Coalition for Content Provenance and Authenticity) CV (Computer Vision) Capability Elicitation Career Modalities Chain of Thought Client AI Cloud AI Cognitive Load Cognitive Offloading Collaboration Compute Use Computer Use Conscience Consent Considerate Display Content Models Content Moderation Context Control Copilot Mode DL (deep learning) DL Engines Data Labeling Data Poisoning Data Privacy Dataset Bias Dataset Curation Design Automation Design Education Design for AI Design for AI/AGI Digital Provenance Digital Twin EUI/AI Embedded AI Embodied AI Emergent Capabilities Empathy with AI Ethics Evaluation Explainable AI Fairness Metrics Fake News Few-Shot Prompting Fine-Tuning Foundation Model Free Speech GOFAI (Good Old-Fashioned AI) GenAI Interns Generative AI Generative Design Grounding Hallucination Harness Human in the Loop Human-Centered AI Human-on-the-Loop Image Generation Image-to-Image Image-to-Text Inference Efficiency Inference Engine Intent Classification Intent Detection Interface JSON Mode Justifiable Risk LLM (Large Language Model) LLMOps (Large Language Model Operations) LLMs (Large Language Models) Latency of Computation Latency of Response Meta-Prompt Meta-Prompting Model Drift Model Hallucination Model Misuse Model Poisoning Model Training Model Use Multi-modal Multi-modal Interface NLP (Natural Language Processing) NSAI (Neural Symbolic AI) NSFW Filter Open Source Open Source AI PEFT (Parameter Efficient Fine-Tuning) Personalization Personalized AI Plan Mode Plans/Planning Post-Training Pre-Training Predictive UI Proactive AI Proactive AI DESIGN Progress Disclosure Prompt Prompt Chaining
Prompt Debugging Prompt Design Prompt Engineering
Prompt Evaluation Prompt Injection
Prompt Injection Mitigation
Prompt Libraries Prompt Literacy Prompt Template
Prompt Versioning Prompting Push vs Pull RAI (Responsible AI) RAS (Retrieval Augmented Generation) RLF RLHF (Reinforcement Learning from Human Feedback) Recommendation Engine Reinforced Learning Reinforcement Learning Response/AI Roles & Tone Rules Safety Filters Semantic Search Shadows Mode Silicon Use Speculative Design for AI Speech Stochastic Prompt Streaming Text Effect Structure Subagents Subtasks Supervised Learning Supervision & Oversight Symbolic AI Synthetic Data Synthetic Users System Prompt Task Delegation Taxonomy of Agents Temperature Text-to-3D Text-to-Code Text-to-Image Text-to-Speech Text-to-Video Throughput Tokens Tool Use Top-k Sampling Toxicity Detection Training Transfer Learning Transformer Transparency Trust Trust Calibration Unlabeled/Raw AI Unsupervised Learning Usability Vector Search Voice Voice Interface Voice Language Model Voice Recognition Weights Workflow Automation Workflows Zero-Shot Prompting
Hey , Reviewed a few of Bruce Sterling’s competitors, and here’s what stood out:
They’re earning considerably higher earnings, and it’s not because of a higher-quality product.
They’ve just fine-tuned: * Precision-focused keywords * High-impact listings * And a few other methods I can show you
Based on what I saw, it appears you could be leaving as much as $35K per month on the table. I prepared a quick breakdown of what they’re doing (and what you can optimize to outperform them).
Would it be out of the question to hop on a quick 15-minute call so I can explain it to you?
