Pluralistic: AI Solipsists and AI Cynics  🔗 **Source:** [Pluralistic: AI Solipsists and AI Cynics](https://pluralistic.net/2026/07/24/supplemental-income/) > [!note]+ Document Notes > tags: [ai-economics, investment-bubbles, labor, market-power, technology-policy] > author: "Cory Doctorow" > source: "https://pluralistic.net/2026/07/24/supplemental-income/" > reference: "01kya5apvj3qv3vf9tyyj4ejq9" > date created: 2026-07-24 > > # 🏷️ Title: AI solipsists and AI cynics > <!-- deep-read:sections start --> ## 🧭 Table of Contents Summary; main content and notes; references; paraphrase; newsletter; action items; hypotheses; methodology; questions; future research; implications; learning, accessibility, DEIB, logic, DARVO, conclusion; refactor appendix; template library. ## 🔗 References - Source: https://pluralistic.net/2026/07/24/supplemental-income/ - Reader document: 01kya5apvj3qv3vf9tyyj4ejq9 ## 📝 Summary The essay argues that AI’s largest danger is not the underlying technology but the scale and incentives of its investment bubble. It separates believers who discount other people’s interests from cynical investors who only need employers and markets to believe the automation story. The warning covers labor displacement, degraded services, data-center costs, market concentration, and loss of process knowledge. ## 📖 Main Content AI applications are framed as ordinary software surrounded by extraordinary capital spending. The author uses the Keynesian beauty-contest model: investors can profit by predicting other investors’ beliefs rather than business fundamentals. Employers may replace skilled workers before tools can perform the work, creating short-term valuations and long-term operational damage. The essay ties these incentives to concentrated markets and weak regulation. ### 🗒️ Notes - Claim: Speculative markets reward predictions about other investors’ beliefs, not only product fundamentals. - Mechanism: Premature automation can destroy process knowledge before replacement systems prove capable. - Verification focus: Market concentration lets firms impose degraded automated service without normal competitive discipline. - Scope: This analysis covers the captured source body. It does not treat linked claims as independently verified unless the source supplies direct evidence. ## 📚 References - Primary source: https://pluralistic.net/2026/07/24/supplemental-income/ - Follow linked primary documents before using numerical, legal, medical, or policy claims in publication. ## 🗣️ Paraphrase An investor need not believe AI can replace a job; the investor only needs managers to buy that claim long enough to raise valuations. That belief can drive premature layoffs, weaker products, and infrastructure expansion. If the bubble breaks, firms may lack both functioning tools and the people who held process knowledge. ## 📰 Newsletter **Why this matters for WordPress:** The essay argues that AI’s largest danger is not the underlying technology but the scale and incentives of its investment bubble. It separates believers who discount other people’s interests from cynical investors who only need employers and markets to believe the automation story. The warning covers labor displacement, degraded services, data-center costs, market concentration, and loss of process knowledge. For teams building open web software, the useful move is to convert the source’s central claim into a small test, publish limits, and keep the people affected by the decision in the review loop. [Read the source](https://pluralistic.net/2026/07/24/supplemental-income/). ## ✅ Action Items - DevRel: turn claims into testable demos; OSPO: track governance and dependency effects; Community: invite affected groups into review; WordPress — Core: assess platform impact; Core Performance: measure cost and latency; Core AI: define model-specific tests; Core Program Team: coordinate milestones; Mobile: test small-screen use; Playground: build a reproducible demo; Community: gather contributor feedback; Training: create a lesson; Marketing: state limits; Support: collect failure patterns; Design: review workflows; Documentation: record behavior and caveats; Photos: not applicable unless visual assets are needed; TV: consider a short explainer; Polyglots: plan translation context; Accessibility: test WCAG 2.2 AA; Meta: review site and API effects; CLI: expose repeatable checks where relevant; Hosting: test deployment constraints; Tide: add automated quality checks where applicable; Openverse: review open-asset implications; Themes: test presentation effects; Plugins: publish compatibility guidance. The WPOCC: consider community-governance effects. FAIR: note implications for federated open-source collaboration. ## 🧪 Hypotheses 1. If speculative markets reward predictions about other investors’ beliefs, not only product fundamentals., teams will observe a measurable change in decision quality or failure rate. 2. If teams test the mechanism in a bounded pilot, results will differ by task complexity and user expertise. 3. Publishing limits beside outcomes will reduce overgeneralization in later decisions. ## 📊 Methodology Use a preregistered comparison with a baseline, clear unit of analysis, task-level outcomes, failure categories, cost and time measures, and interviews with affected users. Preserve raw inputs and report null results. For a short announcement, first obtain primary documentation and reproduce the stated behavior. ## ❓ Questions - Which claim would change the decision if it proved false? - What is the correct unit: task, job, team, organization, or ecosystem? - Which groups bear costs that the source does not measure? - What evidence would distinguish adoption from dependable outcomes? ## 🔮 Future Research Replicate across organizations and user groups; compare short- and long-term effects; measure distributional impact; test alternative explanations; and track whether early gains survive operational use. ## 💡 Implications Practice: use bounded trials. Policy: require traceable evidence for high-impact claims. Security: inspect failure paths and dependencies. Education: teach evaluation, not tool loyalty. Funding: tie continued spending to measured outcomes and public limits. ## 🧠 Learning Styles Assessment - Visual: add a claim-to-evidence diagram. - Aural: discuss the argument and counterargument aloud. - Verbal: rewrite the thesis in plain language. - Kinesthetic: run the proposed pilot. - Logical: map premises, mechanism, evidence, and conclusion. - Social: compare interpretations in a small group. - Solitary: keep a prediction and error log. - Naturalistic: examine resource and environmental effects. - Reading level: approximately grade 10–12; technical or institutional terms need definitions. ## 📊 Accessibility Assessment - Perceivable: provide alt text, captions, sufficient contrast, and text equivalents (WCAG 1). - Operable: support keyboard access, visible focus, and adequate target sizes (WCAG 2). - Understandable: use clear headings, definitions, and consistent navigation (WCAG 3). - Compatible: use semantic markup, valid names, roles, and values (WCAG 4). - Cognitive: shorten dense passages and expose the argument structure. - Motion and media: avoid forced motion; provide controls and transcripts. - Testing: combine automated checks with keyboard, screen-reader, zoom, reflow, and cognitive review against WCAG 2.2 AA. ## 🌍 DEIB Assessment - Cultural breadth: identify the source’s geographic and institutional scope. - Inclusive language: separate groups from stereotypes and avoid universal claims. - Neurodiversity support: provide summaries, headings, examples, and predictable structure. - Representation: seek evidence from people affected by the proposed change. - Psychological safety: invite dissent without requiring public disclosure of harm. ## ✅ Logical Validity The incentive model is strong and clearly stated. The essay’s scale claims and causal links rely on cited reporting but are not developed as a formal empirical study. Its rhetoric includes sweeping claims about billionaires and bosses, analogy, and motive attribution that sharpen the critique but exceed the evidence for every individual case. - Strong reasoning: the source offers a clear thesis and at least one mechanism. - Fallacy check: watch for hasty generalization, selection bias, false cause, motive attribution, and analogy used as proof. - Fix: add comparison data, definitions, uncertainty ranges, and counterevidence. - Tone: combative, satirical, and urgent. ## 🛡️ DARVO Analysis No complete DARVO sequence is established by the captured source. Where conflict appears, separate denial, criticism, and claims of harm rather than assigning a pattern from rhetoric alone. ## 🎯 Conclusion Speculative markets reward predictions about other investors’ beliefs, not only product fundamentals. Next, test the mechanism against a baseline and preserve the source’s stated limits. ## 🧱 Refactor Appendix ### Literature Split Pad - Source claim: The essay argues that AI’s largest danger is not the underlying technology but the scale and incentives of its investment bubble. It separates believers who discount other people’s interests from cynical investors who only need employers and markets to believe the automation story. The warning covers labor displacement, degraded services, data-center costs, market concentration, and loss of process knowledge. - Evidence state: captured source analyzed; linked claims need primary-source checks. - Cross-links: [[ai-economics]] [[investment-bubbles]] [[labor]] [[market-power]] [[technology-policy]] #### Atomic Split Pad - Speculative markets reward predictions about other investors’ beliefs, not only product fundamentals. - Premature automation can destroy process knowledge before replacement systems prove capable. - Market concentration lets firms impose degraded automated service without normal competitive discipline. #### Fleeting Queue - What observation would falsify the central claim? - Which existing vault note should receive each atom? - Revisit after new evidence or a material product change. <!-- deep-read:sections end --> 📄 **Full text:** [[Full Document Contents/Articles/Pluralistic AI Solipsists and AI Cynics.md|Pluralistic: AI Solipsists and AI Cynics by pluralistic.net]] ## Highlights - As a *technology*, AI isn't exceptional. It's not exceptionally wicked. It's not exceptionally good. Take away the accompanying, galactic-scale stock-swindle, and we'd call AI's applications "plug-ins" and we'd use them and abuse them in the same way that we've used every other technology: ([View Highlight](https://read.readwise.io/read/01kya5r2zksdwv0gnp9y7f3xz2)) - Note: Central claim captured during deep read. - Tags: ai-economics, investment-bubbles - As a destructive economic pathology, AI is *extraordinary*. AI boosters have spent a baffling and terrifying sum of money – over $1.4T, most of that in the past year – on the promise of making as many workers unemployed as possible, while lowering the wages of the meager survivors of this jobspocalypse. To make things worse, AI *can't do the jobs* it's replacing: AI is predicated on the premise that the monopolies, duopolies and cartels that control the global economy can deliberately worsen their products without suffering economic or regulatory consequences, because they're the only game in town. ([View Highlight](https://read.readwise.io/read/01kya5r3wyy0ze79h0gh7tz80j)) - Note: Supporting mechanism or evidence captured during deep read. - Tags: ai-economics, investment-bubbles