The author argues that despite improvements in open-weight models, most AI inference will remain in datacenters because local models can't match frontier performance and are actually more expensive to run. Batching hundreds of users' requests together and specialized datacenter GPUs make cloud inference roughly 30x more efficient than running models at home, and users will always prefer the strongest available model in their budget.

Amazon’s contract with Anthropic will move to per-token billing next year, threatening a steep spike in costs for services like Kiro, Quick and Alexa Shopping that use Claude. To curb expenses, Amazon is exploring OpenAI’s models. Meanwhile, Anthropic is deepening ties with Google Cloud and a recent security dispute has driven a wedge between the two.

This issue covers SpaceX’s $6.3 billion deal with Reflection AI to open Project Colossus compute access and OpenAI’s launch of GPT-5.5 Cyber security tools via its Daybreak partner program. It also highlights Alibaba’s HappyHorse video model, Anthropic’s encrypted reasoning in Claude Code, and advances in agentic and open-source AI models.

Microsoft will invest A$25 billion (US$18 billion) by 2029 to expand Australia’s digital infrastructure, AI supercomputing capabilities and cloud capacity. The move aims to boost commercial cloud services and AI/GPU offerings for local customers.

In this Pragmatic Engineer episode, Martin Kleppmann walks through updates in the second edition of Designing Data-Intensive Applications and shares how his LinkedIn experience shaped the book’s core concepts. He breaks down trade-offs in multi-region and cloud architectures, explains why replication still matters more than sharding, and predicts a rise in formal verification and local-first software.

This article explores the evolving role of data engineers over the past 50 years, highlighting their often unnoticed contributions to data infrastructure. It discusses the challenges they face, such as managing dependencies and schema changes, while emphasizing that the core problems remain unchanged despite new tools and technologies.