AI Reading List

Daily curated list of news, articles, and other interesting posts related to AI.

2025-11-06

• OpenAI asks U.S. for loan guarantees to fund $1T AI expansion (investinglive.com)

  OpenAI is seeking US government loan guarantees to support a trillion-dollar AI infrastructure expansion, highlighting the massive costs of scaling advanced AI models. This approach signals a shift toward public sector involvement in funding frontier tech, with far-reaching implications for how large AI projects are financed.

• The trust collapse: Infinite AI content is awful (arnon.dk)

  Thoughtful critique on how the explosion of AI-generated content has eroded trust in digital outreach, making it harder for buyers to identify genuine relationships or credible offers. Emphasizes that sustaining trust now requires genuine human interaction and a clear differentiation from generic, automated messaging.

• AI Slop vs. OSS Security (devansh.bearblog.dev)

  Interesting take on how AI-generated vulnerability reports are overwhelming open source maintainers with false positives, leading to wasted effort and increased burnout. The article highlights that as fake AI-generated reports dominate security submissions, genuine findings become harder to identify, harming the sustainability of open source projects.

• Refund requests flood Microsoft after tricking users into AI upgrades (www.afr.com)

  Microsoft faces a wave of refund requests from Australian customers who felt misled into upgrading to more expensive Office subscriptions with the AI Copilot feature. The company has apologized for a lack of transparency and difficulties in managing compensation for millions of dissatisfied users.

• LLMs Encode How Difficult Problems Are (arxiv.org)

  Interesting analysis of how large language models internally represent the difficulty level of problems, showing a strong alignment with human judgment when trained using annotated examples. The findings suggest that human-labeled difficulty can reliably guide these models during post-training, while model-derived estimates become less accurate as models improve.

• The Learning Loop and LLMs (martinfowler.com)

  Thoughtful reflection on how large language models should be used as brainstorming aids rather than autonomous code generators, highlighting that meaningful software development is a continuous, hands-on learning process. LLMs lower barriers to experimentation but cannot replace the essential human loop of observing, experimenting, and applying knowledge.