Oracle projects up to $95 billion in capital spending for fiscal 2027 to expand its AI-focused cloud data centers, expecting to recoup $20–25 billion from customer repayments. It plans to raise nearly $40 billion through debt and equity, including a $20 billion at-the-market stock issuance, as it vies with Amazon and Microsoft.

• Oracle is guiding to $95B in capex for fiscal 2027, far above the $67.7B Wall Street expected, and it already overshot its FY2026 target ($55.66B vs. $50B goal)
• It's funding this with nearly $40B in new debt and equity, including a $20B at-the-market stock offering, raising leverage concerns
• Shares dropped 8.9% after-hours despite backlog (future contracted revenue) hitting $638B, beating the $592.5B forecast
• CFO warned gross margins will shrink as spending on data centers accelerates, with $70B of the capex being Oracle's own money and $20-25B customer-funded

This daily digest covers SpaceX’s $60 billion stock deal to buy AI coding startup Cursor, Apple’s plan for camera-equipped AirPods and a foldable iPhone in 2027, and Genesis AI’s new industrial robot with LG. It also highlights Snap’s $2,195 AR glasses, AWS’s S3 annotations feature, Meta’s crumbling engineering culture, Anthropic’s talks with Trump officials, and leaked OpenAI finances showing huge losses.

• SpaceX is buying AI coding startup Cursor for $60 billion in stock, expected to close Q3
• OpenAI's leaked financials show revenue nearly quadrupled to $13.07B in 2025, but losses grew from $4.1B to $6.11B as expenses more than doubled
• Apple is reportedly developing camera-equipped AirPods, a foldable iPhone, and a 20th-anniversary model, all targeting late 2027
• Anthropic is negotiating with Trump administration officials over access restrictions after a security bypass was discovered in its latest models

Martin Kleppmann discusses his journey from startups to academia, and the new edition of his book Designing Data-Intensive Applications. They cover trade-offs in modern infrastructure, cloud scalability, distributed system challenges, and emerging topics like formal verification and local-first software.

• Kleppmann rewrote and reorganized Designing Data-Intensive Applications' second edition to add consensus algorithm case studies and deeper cloud failure-handling coverage
• Cloud infrastructure has shifted the core scaling trade-offs from raw hardware capacity to network behavior and API guarantees
• Kleppmann is now researching local-first software algorithms for offline-first collaboration and cryptographic methods for supply-chain transparency that preserve pricing/manufacturing secrecy
• He predicts formal verification will become more important as AI-assisted coding introduces subtle bugs that are hard to catch otherwise

A former Azure engineer details how Microsoft's mismanagement and unrealistic plans jeopardized its relationship with OpenAI and the US government. The article outlines the internal chaos and lack of clarity that led to significant operational failures.

• Azure engineers seriously considered porting Windows features onto an Overlake accelerator card whose hardware specs made the task unrealistic.
• Azure infrastructure runs on 173 management agents with no clear understanding of why or how they're needed, creating major operational risk.
• This mismanagement damaged Microsoft's trust with OpenAI and the US government, key stakeholders reliant on Azure's stability.
• Warnings raised directly with Microsoft leadership went unanswered, reflecting a leadership disconnect tied to the company's massive market value loss.

This article exposes how tool sprawl, fragmented ownership, and “just add more compute” mindsets drive runaway cloud data engineering costs. It shows how central platform teams, cost visibility, data contracts, and quarterly audits can slash spend by up to 60% and offers a 30-day roadmap to get started.

• Engineers in fragmented tool setups waste 60% of their week on plumbing instead of analysis.
• Moving to a platform-team model with built-in cost tracking cuts spend by 40–60% within three months.
• Data contracts force systems to break fast on mismatches instead of silently burning resources on bad output.
• A 30-day plan—cost attribution, pruning idle jobs, platform-team setup, embedded cost reviews—turns chaos into control.