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AI Fails to Win Over the Public

TechCrunch reports that public sentiment toward AI is worsening despite technological progress. Multiple polls and studies (Pew Research, CNBC, Economist/YouGov) show growing consumer concern about AI’s speed, trustworthiness, and social impact. Political actors and local communities are pushing back against AI infrastructure projects — Axios reported a memo from the National Republican Senatorial Committee warning that data centers hurt electoral chances — and publishers report tech firms offering local incentives to ease opposition to data-center builds. Industry leaders including Airbnb’s Brian Chesky and Anthropic’s Dario Amodei acknowledge a trust crisis and argue the sector has not yet delivered tangible benefits for everyday people.

a16z Announces Inaugural Talent Engineer Fellows

Andreessen Horowitz (a16z) announced the inaugural a16z Talent Engineer Fellowship, an 8-week program for talent leaders building next-generation recruiting systems. The cohort comprises 50 fellows drawn from companies including Jack & Jill, Ashby, OpenAI, Airbnb, Clay, Decagon, and Ramp. Many fellows are described as building AI-native talent systems, agentic headhunters, career development platforms, and people-graph infrastructure. The post says thousands of applications were received and includes a sign-up link for updates about future fellowships and a16z opportunities. Publication date on the page is 2026-08-19.

Study: AI visibility often diverges from SEO rankings

Fractl analyzed how three large language models (GPT-4o, Gemini 2.5 Flash, and Claude Sonnet 4.6) referenced brands across eight industries and compared those references to traditional SEO metrics from Ahrefs. While more than 90% of brands showed alignment between search authority and AI visibility, notable outliers existed: about 5% of brands (471) were underrepresented in model responses and about 4% (377) overperformed relative to their SEO footprint. Only 11% of brands were referenced by all three models, and model-specific recall patterns (e.g., Claude favoring SaaS/insurance, Gemini favoring travel/healthcare) create measurement and strategy challenges. The study concludes brands should measure AI recall separately, strengthen third-party corroboration, and fix categorization signals to improve AI recall.