Analysis you can use on Monday.
Long-form, evergreen pieces built from primary sources and public disclosures. These are decision guides for technical leaders: what the technology actually costs, where it breaks, and how to choose between real options. Unlike the daily brief, they are not tied to a single news cycle.
Big Tech
Reading hyperscaler capex: a buyer's guide to cloud earnings
Hyperscaler earnings are the closest thing to a forward price list for compute. What capex, depreciation, and backlog reveal about capacity and price.
Future Tech
What N2, 18A, and HBM roadmaps mean for refresh plans
Node names are marketing; shipment dates are commitments. How N2, 18A, HBM4, and packaging constraints should shape a server refresh that lasts five years.
Big Tech
Antitrust remedies and your procurement calendar
Remedies now arrive on a procurement timetable. Here is what the DMA, US platform cases, and consent decrees actually change for buyers choosing enterprise platforms.
Startups & Venture
AI gross margin: the number investors will underwrite
Investors do not underwrite AI revenue, they underwrite the margin it carries. A cost-of-revenue boundary, three engineering levers, and a board-tested model.
Cloud & DevSecOps
Zero trust: the controls auditors actually test
Architecture diagrams do not survive a control walkthrough. These are the identity, device, segmentation, logging, and key controls that frame questionnaires probe.
Future Tech
Post-quantum migration: a realistic 2027-2032 timeline
NIST finalized the post-quantum standards in 2024. The hard part is inventory, hybrid deployment, and vendor deadlines. A phase-by-phase plan for the years that matter.
Big Tech
Platform risk: engineering around hyperscaler policy changes
Deprecated APIs, license shifts, AI-training terms, and quiet quota changes all arrive on a calendar. How to design systems that survive the notice period.
Future Tech
The data-center power crunch and what it does to cloud prices
Interconnection queues, PPAs, SMRs, and cooling decide what a kilowatt-hour costs a cloud provider. Here is how that reaches your compute bill, and when.
Startups & Venture
AI vendor due diligence: 25 questions to ask first
Model provenance, data retention, evaluations, SLA, and pricing cliffs. The questions that separate a serviceable vendor from an expensive dependency.
AI & Machine Learning
AI agents in production: seven failure modes and fixes
Tool misuse, injection, loops, stale context, permission creep, cost blowups, silent regressions. What each looks like in production, and the guardrail that catches it.
AI & Machine Learning
RAG or long context: choosing a retrieval design
Million-token windows did not retire retrieval. Cost, latency, and where models actually attend decide the architecture, usually a hybrid of both.
AI & Machine Learning
Open weights or closed APIs: a CTO framework
The choice is not ideology. Four constraints (data, customization, compliance, volume) decide it, and self-hosting math is harsher than most decks admit.