AI and Jevons’ Paradox: Why Making AI Cheaper May Make Us Use More of It
There is a strange paradox at the heart of the AI revolution. We keep making AI dramatically more efficient. Models require less compute. Inference costs are falling. Hardware is becoming more capable. Techniques such as quantization, distillation, batching, and better model architectures allow us to get more intelligence out of every unit of compute. And […]
Is AI eating your budget? We wrote the playbook. Literally.
Ask a CFO what kept them awake in 2024, and you’d hear about cloud spend, headcount, or the next funding round. Ask that same CFO today, and the answer has changed: AI token costs. The examples are no longer hypothetical. Uber rolled Claude Code out to 5,000 engineers. Within four months, the company had burned […]
FinOps Platforms That Help Developers Find and Fix Idle Cloud Resources in Real Time
Actually, only Atmoz does this in real time. It detects orphaned or idle resources the moment they’re created. It delivers a one-click fix inside Slack, Teams, or the IDE. Other tools flag idle resources only after they’ve already burned weeks of cost. Idle Resources Are the Easiest Waste to Prevent – and the Hardest to […]
Cloud and AI Costs Happen in Real Time. Cost Control Should Too.
Every time an engineer provisions cloud infrastructure, triggers an AI agent, submits a prompt, or routes a workload through a model, they’re making a financial decision. The problem is they have zero visibility into how much those decisions are actually going to cost. The cost begins immediately. Yet most organizations do not understand the financial […]
