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MIT Technology Review’s The Download highlights a paradox: people say they dislike AI yet continue to engage with it. The newsletter also previews EmTech Future 2026 and mentions an interview with the CEO of Springboards about a purpose-built LLM.
A TechCrunch report says an AI hallucination nearly triggered a U.S. military operation. A GovAI research scholar warned service members must understand uncertainty inherent to large language models.
MIT Technology Review reports that prominent AI figures—Dario Amodei, Sam Altman, Elon Musk, and Demis Hassabis—now share alarm about the latest generation of large language models. The newsletter frames the industry mood as turning toward existential concern and discusses related developments like whistleblowing and biomedical work.
MIT Technology Review reports that children can learn language in ways that currently outpace large language models, despite LLMs being trained on vastly larger datasets. The piece highlights both the scale of data LLMs use and the mystery of why human learners remain more efficient in some respects.
MIT Technology Review discusses how the sudden arrival of powerful chatbots surprised many schools and prompted a need for policies and practices to guide classroom use. The piece is part of the Making AI Work newsletter and highlights practical approaches to applying LLMs in education.
Industry expectations that AI will soon largely improve itself are widespread, citing LLMs' abilities to write code, create synthetic training data, and optimize hardware. MIT Technology Review reports that despite these capabilities, forecasts of rapid recursive self-improvement may be premature.
TechCrunch AI reports that Amazon is destroying rare physical books to obtain text for training large language models. The article says rare books are valuable because models have already absorbed much publicly available online text.
MIT Technology Review reports that a new wave of startups is pursuing innovations beyond the transformer architecture that has powered large language models for nine years. The piece also notes shifts in AI academic research priorities, suggesting a changing landscape for model design and study.
MIT Technology Review profiles startups aiming to advance large language model design and applications, tracing the field back to the 2017 ‘Attention Is All You Need’ paper. The piece previews trends and companies that could shape future LLM research and products.
A team of researchers argues in a paper presented at the International Conference on Machine Learning that it is impossible to make large language models fully secure against hacks due to a fundamental flaw in how they work. The claim, reported by MIT Technology Review, highlights broad safety implications for LLM deployment.
New research reported by MIT Technology Review says large language models not only absorb human biases from training data but can also develop their own biases when used for hiring. The findings raise questions about whether AI resume screening will judge applicants fairly.
Anthropic researchers built a diagnostic called the Jacobian lens that gives a clearer view of what Claude attends to when reasoning. Their probe revealed structured internal regions used for concept-like processing, with findings ranging from expected to unsettling.
