This week, we look at how the 2026 AI infrastructure boom is diverting silicon, memory production, and electricity away from consumer electronics, raising prices and worsening digital inequality.

We look at Bloomberg’s report that Alphabet, Amazon, Meta, and Microsoft budget about $650B in 2026 capex, dwarfing other industries, while the memory market’s three dominant suppliers (Samsung, SK Hynix, Micron) pivot toward high-bandwidth memory for AI.

OpenAI’s Stargate agreements are described as consuming up to 900,000 DRAM wafer starts per month (~40% of global output), and Micron exits consumer memory (ending Crucial). TrendForce projects steep 2026 price spikes for DRAM and NAND, driving IDC forecasts of falling smartphone and PC shipments, higher device prices, downgraded specs, and strained gaming GPU supply.

We also discuss the link between data centre's to rising electricity bills and US tariffs to further price increases, with refurbished devices as a limited stopgap and relief unlikely before 2027–2028.

• Priced Out by AI: The Memory Chip Crisis Hitting Every Consumer — SmarterArticles

The global agreement on AI ethics (fairness, transparency, accountability) has not translated into enforcement, creating a widening gap between principles and practice.

Reviews of hundreds of guidelines show strong convergence on stated values, but major divergence on interpretation and implementation, enabling “ethics washing,” illustrated by Google’s 2020 firing of Timnit Gebru and later Margaret Mitchell.

Industry adoption of generative AI is rapid while governance lags, especially as agentic systems spread. Regulatory responses are uneven: the EU AI Act phases enforcement through 2027, while the US is fragmented and contested between federal policy and state laws like Colorado and NYC rules. Real-world harms persist in hiring, housing, and biometric surveillance (Workday, SafeRent, Clearview), with slow legal remedies and documented bias in studies.

Audits are costly, time-limited, and structurally insufficient, and there is critical need for anticipatory, well-resourced, iterative governance with meaningful penalties and broader transparency.

This episode examines the rise of "vibe coding," a term coined by Andrej Karpathy in February 2025 to describe AI-assisted development characterised by accepting changes without understanding them, and argues that industry-wide adoption is creating hidden technical debt and risk. It cites rapid growth in AI-generated code across startups and major firms, then highlights a 2025 Lovable platform incident in which misconfigured database security exposed sensitive user data. It also discusses METR's July 2025 study finding that AI increased task completion time by 19% despite developers perceiving a speedup, and Stack Overflow's 2025 survey showing high AI use but low trust. GitClear data indicates surging code cloning and collapsing refactoring, accelerating debt likened to a "credit card." A Stanford 2025 payroll study reports a sharp decline in junior developer employment, threatening the pipeline of tacit expertise needed to maintain AI-built systems, and the episode proposes governance, reviews, specification-first workflows, education, and preserving junior pathways.

In this episode we look at how conversational AI is subtly but significantly reshaping human language, relationships, and social capacity.

Max Planck researcher Hiromu Yakura found that ai-favoured words like “delve” and “realm” increased up to 51% in unscripted academic YouTube and podcast speech after ChatGPT’s 2022 launch, suggesting a feedback loop in which AI patterns transmit back into human minds.

While AI boosts efficiency in banking, healthcare scheduling, and customer service, an expanding market projected to save massive labour costs, the removal of interpersonal “friction” may erode social skills.

Sherry Turkle warns AI offers “illusion of intimacy without the demands,” and neuroscience and education research suggests anthropomorphism (ELIZA effect), potential social-brain changes, and more passive child engagement.

A 2025 OpenAI–MIT study links heavier ChatGPT use to higher loneliness and dependence; surveys show widespread teen AI-companion use, declining trust when AI is suspected in messages, public concern about relationships and creativity, and rising stress for remaining human contact-centre agents, alongside limited but real benefits from thoughtfully designed systems.

In this episode we examine the privacy and ethical risks in dating apps, focusing on Tinder’s AI assistant, Chemistry, which asks to scan users’ camera rolls to improve matching amid Match Group’s subscriber decline.

The camera roll contains uniquely uncurated, highly sensitive data that modern computer vision can infer into identities, social graphs, health, location, finances, and protected characteristics, making “opt-in” consent coercive when refusal may mean worse matches.

Mozilla’s 2024 review and multiple incidents (unprotected explicit-image storage across five apps, Tea’s breach, Grindr sharing HIV status, Bumble’s biometric settlement), shows that dating apps are exceptionally breach-prone. We contrast the existing legal protections (GDPR, Norway’s Grindr fine, Tinder inquiry, Illinois BIPA) with under-enforcement, and talk about privacy-preserving alternatives (on-device processing, federated learning, differential privacy) that apps avoid for economic, advertising-driven reasons.

We also talk about the culture of “consent theatre” and discuss harms that include coercive control, discrimination, engagement exploitation, and nonconsensual exposure of bystanders in users’ photos.