Article Index

When AI creates value, who gets it? We cover redistribution — of wealth, work, and power.

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Explainers

Deep dives into AI systems, written for humans — not "what is a neural network" but "how does your bank's fraud detection system decide you're suspicious?"

• How Language Models Actually Work — The probability machine that powers everything
• The Training Pipeline — From raw text to chatbot, stage by stage
• What Model Cards Tell You (And What They Don't) — Reading between the lines of AI documentation
• Running AI Locally — What it takes to run models on your own hardware
• AI and Consumer Surplus — When AI makes things cheaper, who captures the savings?
• The Productivity Paradox — Why AI productivity gains aren't showing up in the data
• The Shape of AI Displacement — Which jobs, which workers, which places
• How Reasoning Models Actually Work — Test-time compute, chain-of-thought, and the cost of thinking
• What Fine-Tuning Really Does — Customizing AI models and who gets to do it
• The Economics of Training a Frontier Model — Why billion-dollar training runs shape who controls AI
• Synthetic Data and the Ouroboros Problem — When AI trains on AI, quality eats itself
• How Retrieval-Augmented Generation Changes Who Needs What — RAG, vector databases, and the new enterprise AI stack
• AI in Healthcare: Who Benefits from the Diagnostic Revolution — Personalized medicine meets unequal access
• Agentic AI and the Autonomy Question — Autonomous AI systems and what they mean for work and control
• Recursive Self-Improvement: The Feedback Loop That Keeps AI Leaders Up at Night — When AI accelerates AI research, who controls the loop?
• World Models vs. Large Language Models: Two Visions of Machine Intelligence — JEPA, world models, and whether intelligence needs trillion-parameter models
• Physical AI: When Machines Learn to Touch the Real World — Robotics, embodied intelligence, and who gets displaced when AI goes physical
• Multimodal AI: When Machines See, Hear, and Read at Once — Cross-modal AI and the expanding blast zone of automation

Common Pitfalls

One misconception at a time, dismantled carefully.

• No, AI Doesn't Learn Like a Brain — The neuroscience analogy breaks down fast
• Why Removing Bias From Data Doesn't Remove Bias — The limits of data cleaning
• The Autonomy Illusion — AI doesn't decide; people do
• AI Won't Solve Climate Change by Itself — The gap between optimization promises and political reality
• The Scaling Laws Plateau — Why "just make it bigger" stopped working
• Open Weights Are Not Open Source — What "open" AI actually shares and what it keeps
• AI Benchmarks Don't Measure What You Think — Leaderboards, contamination, and the vibes gap
• The Disruption Narrative Hides Who Gets Hurt — Creative destruction isn't creative for everyone
• The Two-Species Fallacy: Why Treating AI as a Life Form Obscures Power — The AI-as-species metaphor hides who actually controls these systems

Paper Reviews

Accessible summaries of significant AI papers and books. What they found, why it matters, what they got wrong.

• Paper Review: The Wrong Kind of AI? — Acemoglu on so-so automation and why policy matters
• Paper Review: Datasheets for Datasets — Gebru et al. on documenting training data
• Paper Review: Gender Shades — Buolamwini and Gebru on racial and gender bias in facial recognition
• Book Review: Ghost Work — Gray and Suri on the hidden human labor behind AI
• Paper Review: AI Market Concentration — Evidence on market power in AI industries
• Paper Review: Open (For Business) — What "open source" AI actually means for competition
• Paper Review: On the Dangers of Stochastic Parrots — Bender et al. on the risks of large language models
• Book Review: Atlas of AI — Kate Crawford on AI's material supply chains
• Book Review: Automating Inequality — Virginia Eubanks on the digital poorhouse
• Paper Review: GPTs Are GPTs — Eloundou et al. on AI exposure across the labor market
• Paper Review: The AI Index Report 2025 — Stanford's annual survey of AI trends and what it reveals about concentration
• Book Review: Power and Progress — Acemoglu and Johnson on why technology doesn't automatically benefit everyone
• Paper Review: Sparks of AGI — A Premature Conclusion — Microsoft's GPT-4 claims and why they don't hold up
• Paper Review: The Distributional Effects of AI — Korinek on who wins and who loses as AI transforms the economy
• Article Review: Engels' Pause and the Permanent Underclass — O'Laughlin on AI as the new Industrial Revolution — and who pays

Documentation Reviews

Critical reads of AI product documentation, terms of service, and model cards.

• Doc Review: The Llama License — What Meta's "open" license actually says
• Doc Review: Claude's Model Card — What Anthropic discloses (and doesn't) about Claude
• Doc Review: OpenAI's Terms of Service — The fine print of API access
• Doc Review: Gemini's Safety Documentation — What Google discloses about Gemini
• Doc Review: DeepSeek's Technical Report — What China's breakout AI lab reveals (and conceals)
• Doc Review: The White House AI Executive Order — The most ambitious US AI policy and why it didn't last
• Doc Review: AI and Human Civilization — Strategic Foresight — Bougueng's open letter on the post-cybernetic era, institutional decoherence, and cognitive inequality

News

Short, opinionated takes on AI news — who benefits, who pays, what to watch.

• Say No to Suno: Artist Coalition Targets AI Music's Royalty Drain — The music industry's frontline fight against AI
• DeepSeek and the Price of Open AI — How a Chinese lab shook Silicon Valley's cost assumptions
• The EU AI Act Takes Effect: What's Actually Changing — Implementation begins, compliance costs emerge
• AI Copyright Rulings Are Shaping the Training Data Economy — Legal battles that will define who owns AI's inputs
• Nvidia's $3 Trillion Question — The chokepoint company of the AI era
• OpenAI's For-Profit Conversion: The Mission Pivot — From "benefit all of humanity" to shareholder returns
• Big Tech's AI Layoffs: Spending Billions, Cutting Thousands — The paradox of simultaneous investment and displacement
• The AI Valuation Race: When Hype Meets Capital — Billion-dollar bets on companies that haven't turned a profit
• The Copyright Status of AI-Generated Art: Still Unresolved — Can you own what a machine creates?
• AMI Labs: LeCun's Billion-Dollar Bet Against LLMs — Yann LeCun's world-model startup and what it means for AI's competitive landscape

Policy & Governance

AI regulation, standards, and institutional choices. Who decides, and how.

• The EU AI Act Explained — The world's first comprehensive AI regulation
• Who Regulates Foundation Models — The current patchwork of oversight
• Public AI — What public alternatives to corporate AI could look like
• The Influence Machine — How AI companies lobby to shape regulation
• China's AI Strategy: State-Directed Development as an Alternative Model — The other approach to AI development
• Democratic AI Governance — Models for public participation in AI decisions
• Regulatory Capture in AI — When regulators serve the regulated
• Stock Buybacks and AI — Where AI profits actually go
• The Digital Divide in AI — Who gets access and who gets left behind
• AI Executive Orders: What Changed and What Didn't — US federal AI policy and its fragility
• State-Level AI Regulation: The Patchwork Problem — 50 states, 50 approaches, one compliance nightmare
• The AI Safety Summit Circuit: Diplomacy or Theater? — International summits and what they actually produce
• Universal Basic Income and AI: The Debate Gets Real — From thought experiment to policy proposal
• AI and Antitrust: Why Competition Law Struggles with Foundation Models — Market concentration and the limits of existing frameworks
• The Sovereign AI Movement — Why nations want their own AI and what it means for redistribution
• AI Cooperatives: Alternatives to Corporate Models — Democratic ownership of AI development
• AI and Democracy: Deepfakes, Bots, and Informed Consent — When AI reshapes the information environment voters need
• Institutional Decoherence: When Technology Outruns Governance — Why political, social, and cultural systems can't keep up with AI
• Artificial Dissuasion: Can Nuclear Deterrence Logic Apply to AI? — Whether game theory and deterrence frameworks can prevent catastrophic AI deployments
• The Venture Capital Trap: How Funding Structures Shape What AI Gets Built — Why VC incentives systematically bias AI toward extraction over public goods
• The Academic Brain Drain: Who Pays to Train AI's Best Minds? — Public universities train researchers, private labs capture the returns
• Europe's AI Paradox: Regulating What You Don't Build — Europe writes the rules but doesn't own the infrastructure

Systems & Access

The infrastructure, standards, and economics that shape who can build with AI.

• The Redistribution Stack — Players, standards, and the battle between open and captured
• Who Owns the Training Data? — Commons, enclosure, and the legal battles
• Compute Is the New Oil — Why training costs create natural monopolies
• Open Source vs. Open Weights — What "open" actually means for AI
• The API Economy — Access, dependency, and platform power
• How Technology Gets Distributed: Models That Shape Who Benefits — Historical patterns of technology diffusion
• Pricing Power: How API Costs Shape Who Can Build — The economics of AI access
• The Frontier Lab Oligopoly — The small group that controls frontier AI
• The Hardware Bottleneck — NVIDIA, chips, and the physical constraints on AI
• The Inference Tax: How API Pricing Creates Perpetual Rent — The ongoing cost of using AI
• The Magnificent Seven — Big Tech's AI consolidation
• The Data Center Boom and Who Pays the Energy Bill — AI's insatiable appetite for power and water
• AI Chip Export Controls: Technology as Geopolitics — How semiconductor restrictions reshape global AI development
• The Rise of Small Language Models — When smaller, cheaper models are good enough
• Who Controls the AI Supply Chain — From cobalt mines to cloud APIs, every chokepoint mapped
• Edge AI and the Promise of Local Compute — On-device inference and what it means for independence
• AI and Education: Tutoring, Testing, and Who Gets Left Out — Personalized learning meets unequal access
• The Environmental Cost of AI — Energy, water, carbon, and who bears the burden
• Cryptographic Post-Economies: Can Token Systems Redistribute AI Value? — Whether blockchain and tokens can distribute AI-generated value or just reproduce existing power

Labor & Work

AI and the future of work — displacement, augmentation, and who has power in the transition.

• The Hidden Labor of AI — Ghost work, data labeling, and the humans behind automation
• The Automation Debate — Acemoglu vs. Brynjolfsson on what AI means for jobs
• AI and the Gig Economy — How AI reshapes platform work
• The Manager Is an Algorithm — Algorithmic management and workplace surveillance
• Collective Bargaining in the Age of AI — How workers organize around AI deployment
• The White-Collar Displacement Wave — When AI comes for knowledge workers
• AI Coding Assistants and the Future of Software Work — Copilots, code generation, and the junior developer pipeline
• Content Moderation's Mental Health Crisis — The psychological toll of making AI safe
• The RLHF Workforce: Who Trains the Trainers — Inside the invisible labor that shapes AI behavior
• Freelance Markets After Generative AI — The canary in the coal mine for AI labor displacement
• Data Labeling in the Global South — Wage arbitrage and the geography of AI labor
• AI and the Creative Industries: A Redistribution Case Study — Music, art, writing, and film as ground zero

Projects

Tools, collectives, and initiatives advancing AI redistribution. These are the efforts worth knowing about — and supporting.

• WebLLM — Browser-based inference that puts AI on your device
• Hugging Face — The open model platform reshaping AI access
• Ollama — Run AI locally with one command
• Model Context Protocol (MCP) — Open standard for AI tool integration
• Masakhane — African researchers building African NLP
• EleutherAI — Open research collective training open models
• BLOOM — Multilingual model built by 1,000+ researchers
• Common Crawl — The open web archive powering AI training
• AI Now Institute — Policy research holding AI accountable
• Algorithmic Justice League — Fighting algorithmic harm through research and advocacy

Community

Contributors, conversations, and open questions.

• About Redistributed — What we're building and why
• How to Contribute — Ways to help
• Who Builds AI for the Rest of the World — Languages, regions, and communities left behind by mainstream AI

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Reading Paths

New to AI systems? Start with How Language Models Actually Work, then The Training Pipeline, then No, AI Doesn't Learn Like a Brain, then How Reasoning Models Actually Work.

Interested in policy? Start with Who Regulates Foundation Models, then The EU AI Act Explained, then Regulatory Capture in AI, then The Influence Machine, then AI Executive Orders: What Changed and What Didn't.

Skeptical of AI hype? Start with The Autonomy Illusion, then The Scaling Laws Plateau, then AI Benchmarks Don't Measure What You Think, then Paper Review: Sparks of AGI — A Premature Conclusion, then The Disruption Narrative Hides Who Gets Hurt.

Following the money? Start with The Redistribution Stack, then The Magnificent Seven, then The Frontier Lab Oligopoly, then Stock Buybacks and AI, then The AI Valuation Race.

Interested in infrastructure? Start with Compute Is the New Oil, then The Hardware Bottleneck, then Who Controls the AI Supply Chain, then The Data Center Boom, then Edge AI and the Promise of Local Compute.

Concerned about labor? Start with The Hidden Labor of AI, then The White-Collar Displacement Wave, then The RLHF Workforce, then Content Moderation's Mental Health Crisis, then Freelance Markets After Generative AI.

Want the research foundations? Start with Book Review: Power and Progress, then Paper Review: The Wrong Kind of AI?, then Paper Review: GPTs Are GPTs, then Paper Review: The Distributional Effects of AI, then Article Review: Engels' Pause and the Permanent Underclass.

Thinking about alternatives? Start with Public AI, then AI Cooperatives, then The Sovereign AI Movement, then Running AI Locally, then Open Source vs. Open Weights.

Watching the global picture? Start with Who Builds AI for the Rest of the World, then Data Labeling in the Global South, then The Digital Divide in AI, then AI Chip Export Controls, then The Sovereign AI Movement.

Tracking creative industries? Start with AI and the Creative Industries, then Say No to Suno, then AI Copyright Rulings, then The Copyright Status of AI-Generated Art, then Freelance Markets After Generative AI.