The Majority AI View  ## Metadata - Author: [[Anil Dash]] - Full Title: The Majority AI View - Category: #articles - Document Note: --- tags: [AI, Tech industry, Ethics, Governance, Hype cycles] author: Anil Dash source: anildash.com reference: [https://www.anildash.com//2025/10/17/the-majority-ai-view/] date created: 2025-10-20 10:00 --- # 🏷️ Title: The Majority AI View ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ### 🔗 References - Include links cited in or relevant to the article. ## 📝 Summary - What happened: The author argues most working technologists see AI/LLMs as useful but overhyped, coercively pushed, and discussed in ways that ignore valid critiques and alternatives. - Why it matters: Hype crowds out pragmatic adoption and “good AI” paths (consent, sustainability, decentralization), narrowing the future. - Key stakeholders: Tech workers, managers, Big AI firms, creators, policymakers, users. - Risks and tradeoffs: Chilling effects from layoffs and conformity; reputational, regulatory, and social harms if hype dictates strategy. - Bottom line: Treat AI as normal tech—subject to standard scrutiny, controls, and sober evaluation—rather than a cult or inevitability. ## 📖 Main Content - Core arguments: - Working technologists broadly agree: “Technologies like LLMs have utility,” but overhype and pressure “make it very difficult to focus on legitimate uses.” - Media amplifies Big AI narratives while sidelining insiders’ critiques—even though many early warnings came from within big companies. - Power dynamics and layoffs chill dissent; moderate viewpoints feel career-risky. - Ignoring the moderate majority constrains better futures: consentful data, greener systems, decentralized architectures. - Context and definitions: - LLMs: Large language models that generate text and can “hallucinate” incorrect outputs. - “Normal technology”: Framing AI like other software—evaluated with standard risk, ethics, and governance practices. - Uncertainties or contested claims: - The exact size of the “majority” is asserted qualitatively. - Feasibility timelines for consentful, sustainable, decentralized AI vary by domain. ### 🗒️ Notes - “Most people who actually have technical roles... are fluent in the latest technologies like LLMs” but aren’t the “big, loud billionaires.” - “Technologies like LLMs have utility, but the absurd way they've been over-hyped... make it very difficult to focus on legitimate uses.” - Desire: treat AI as “normal technology” (hat tip to Arvind Narayanan and Sayash Kapoor). - Media largely repeats Big AI narratives; insider critiques get muted, despite whistleblowers and fired researchers. - Extremist leadership narrows possibilities; alternatives are possible: consent-based data, sustainability, decentralization. - Layoffs and fear enforce conformity; moderate stances feel risky. - “Very few agree with the hype bubble... the mainstream of tech culture is thoughtful, nuanced and circumspect.” ## 📚 References - The Majority AI View (https://www.anildash.com//2025/10/17/the-majority-ai-view/) - AI Snake Oil by Arvind Narayanan and Sayash Kapoor (https://www.aisnakeoil.com/) - OpenAI (https://openai.com/) - Anthropic (https://www.anthropic.com/) - Google AI (https://ai.google/) ## 🗣️ Paraphrase - Paragraph A: Most practitioners know LLMs and find them useful in places, but hype and coercion obstruct real value. - Paragraph B: Media boosts Big AI stories, sidelining insider critiques, which limits better paths like consentful and sustainable AI. ## 📰 Newsletter - This week: a grounded take on AI adoption for builders. The piece argues AI is “normal tech,” not a religion—focus on clear use cases, consentful data, sustainability, and decentralization. For WordPress folks: pilot AI where it measurably improves editorial workflows, translation, or support triage, and skip the hype. Useful reads: AI Snake Oil’s pragmatic analyses and Core Performance updates. Keep governance tight: data provenance, model evals, rollback plans. ## ✅ Action Items #### DevRel - Publish pragmatic AI integration guides with measurable KPIs and rollback plans. - Run workshops on evaluation sets, prompt hygiene, and error handling. - Maintain examples and quickstarts focused on high-SNR use cases. - Track campaign attribution to real developer adoption, not vanity metrics. - Moderate forums to welcome skeptical, evidence-based feedback. #### OSPO - Define contribution policies for AI-assisted code and provenance. - Enforce license scanning and AI training-data compliance. - Establish vuln intake and model/security embargo procedures. - Maintain SBOMs including model artifacts and data sources. - Offer consultation hours on AI governance and legal guardrails. - Fund upstream projects enabling consentful datasets and verification. - Report AI governance KPIs to executives quarterly. #### Community - Maintainers: add evaluation gates; document non-goals. - Contributors: first-issue rotations on testing/evals. - Sponsors: fund consentful data pipelines and audits. - Ecosystem: practice coordinated disclosures for AI incidents. #### WordPress - Core: define AI-related triage and acceptance criteria. - Core Performance: track latency/cost regressions from AI features. - Core AI: guardrails, eval sets, phased pilots, sunset clauses. - Core Program Team: set cross-team rituals and retros. - Mobile: offline-first fallbacks; error UX for AI failures. - Playground: reproducible AI bug labs and sandboxes. - Community: meetup kits on pragmatic AI. - Training: lesson paths on evaluation and governance. - Marketing: avoid hype; publish case studies with metrics. - Support: SLAs for AI misclassification and escalations. - Design: AI feature discoverability, consent prompts. - Documentation: examples-first, failure modes, limits. - Photos/Openverse: provenance, opt-out/opt-in pathways. - Accessibility: review AI impacts on WCAG conformance. - Meta/CLI/Hosting/Tide/Themes/Plugins: provenance checks, telemetry, scoring transparency. #### The WPCC - Programs: stipends for eval-set maintainers. - Projects: governance clinics; “AI incident” tabletop drills. - Funding: matched grants for consentful data initiatives. ### FAIR - Technical independence: local signatures, provenance checks. - Policies: funding transparency, sunset clauses, anti-capture. - Productized governance: charters, operator agreements, scorecards. ## 🧪 Hypotheses - H1: Teams using “AI as normal tech” governance see fewer production incidents than hype-driven adopters. - H2: Consentful training data reduces legal risk without materially degrading task performance in targeted domains. - H3: Clear evaluation sets improve AI feature retention and NPS. - H4: Public layoffs correlate with reduced employee willingness to voice AI concerns. - H5: Decentralized AI deployments reduce single-vendor outage impact. ### 📊 Methodology - H1/H3: Controlled rollout with A/B teams; track incident rate, NPS, retention. - H2: Compare models trained on consentful vs. mixed data; measure accuracy, legal events. - H4: Survey analysis pre/post layoff events; anonymity and longitudinal tracking. - H5: Chaos experiments simulating vendor outages; measure recovery time. ### ❓ Questions - Where do AI features actually outperform status quo with acceptable risk? - How to operationalize consent and provenance at scale? - What incentives shift media coverage toward practitioner evidence? ### 🔮 Future Research - Standardized evaluation suites for CMS/editorial AI tasks. - Energy and carbon accounting standards for AI features. - Federated, decentralized inference patterns for the web. ### 💡 Implications - P - Summary: A blog about making culture. Since 1999. - URL: https://www.anildash.com//2025/10/17/the-majority-ai-view/ Even though AI has been the most-talked-about topic in tech for a few years now, we're in an unusual situation where *the most common opinion* about AI within the tech industry is barely ever mentioned. Most people who actually have technical roles within the tech industry, like engineers, product managers, and others who actually make the technologies we all use, are fluent in the latest technologies like LLMs. They aren't the big, loud billionaires that usually get treated as the spokespeople for all of tech. And what they all share is an extraordinary degree of consistency in their feelings about AI, which can be pretty succinctly summed up: ##### Technologies like LLMs have utility, but the absurd way they've been over-hyped, the fact they're being forced on everyone, and the insistence on ignoring the many valid critiques about them make it very difficult to focus on legitimate uses where they might add value. What's amazing is the reality that virtually *100% of tech experts* I talk to in the industry feel this way, yet nobody outside of that cohort will mention this reality. What we all want is for people to just treat AI as a "[normal technology](https://www.normaltech.ai/p/ai-as-normal-technology)", as Arvind Naryanan and Sayash Kapoor so perfectly put it. I might be a little more angry and a little less eloquent: stop being so goddamn creepy and weird about the technology! It's just tech, everything doesn't have to become some weird religion that you beat people over the head with, or gamble the entire stock market on. #### AI Hallucinations If you read mainstream media about AI, or trade press within the tech industry, you'll basically only hear hype repeating the default stories about products from the handful of biggest companies like OpenAI, Anthropic, Google, and the like. Once in a while, you might hear some coverage of the critiques of AI, but even those will generally be from people outside the tech industry, and they will often solely be about frustrations or anger with the negative externalities of the centralized Big AI companies. Those are valid and vital critiques, but it's especially galling to ignore the voices within the tech industry when the first and most credible critiques of AI came from people who were working within the big tech companies and then got pushed out for sharing accurate warnings about what could go wrong. Perhaps the biggest cost of ignoring the voices of the reasonable majority of those in tech is how it has grossly limited the universe of possibilities for the future. If we were to simply listen to the smart voices of those who aren't lost in the hype cycle, we might see that it is *not* inevitable that AI systems use content without the consent of creators, and it is *not* impossible to build AI systems that respect commitments to environmental sustainability. We can build AI that [isn't centralized under the control of a handful of giant companies](https://www.youtube.com/watch?v=AhW5M18cvGM). Or any other definition of "[good AI](https://www.anildash.com/2025/05/02/what-would-good-ai-look-like/)" that people might aspire to. But instead, we end up with the *worst*, most anti-social approaches because the platforms that have introduced "AI" to the public imagination are run by authoritarian extremists with deeply destructive agendas. And their extremism has had a profound chilling effect within the technology industry. One of the reasons we don't hear about this most popular, moderate view on AI within the tech industry is because **people are afraid to say it**. Mid-level managers and individual workers who know this is the common-sense view on AI are concerned that simply saying that they think AI is a normal technology like any other, and should be subject to the same critiques and controls, and be viewed with the same skepticism and care, fear for their careers. People worry that not being seen as mindless, uncritical AI cheerleaders will be a career-limiting move in the current environment of enforced conformity within tech, especially as tech leaders are collaborating with the current regime to punish free speech, fire anyone who dissents, and embolden the wealthy tycoons at the top to make ever-more-extreme statements, often at the direct expense of some of their own workers. This is all exacerbated by the awareness that hundreds of thousands of technical staff like engineers have been laid off in recent times, often in an ongoing drip of never-ending layoffs, and very frequently in an unnecessarily dehumanizing and brutal process intended to instill fear in those who remain at the companies afterward. In that kind of context, it's understandable that people might fear telling the truth. But it's important to remember that *there are a lot more of us*. And for those who aren't insiders in the tech industry, it's vital that you understand that you've been presented with an extremely distorted view about what tech workers really think about AI. Very few agree with the hype bubble that the tycoons have been trying to puff up. There are certainly a group of hustle bros on LinkedIn or social media trying to become influencers by repeating the company line, just as they did about Web3 or the metaverse or the blockchain (do they still have .ETH after their names?), but the mainstream of tech culture is thoughtful, nuanced and circumspect., The Majority AI View, Anil Dash, 01k80zdwstd8zbjk524n1paf1x.