Apple recently raised prices across most of their devices, with the iPhone the only exception. The reasoning behind these hikes isn’t mysterious. Memory shortages driven by AI datacenters’ insatiable demand for DRAM have pushed manufacturers to prioritize enterprise AI over consumer hardware. A $30,000 accelerator chip simply offers better margins than a $5,000 graphics card. Currency fluctuations and import duties have also played a role, but the primary driver is the component crunch.
We are beginning to see a scaling and cost crisis in frontier LLMs, especially in the coding agent space.
OpenAI, Anthropic, and Gemini have all increased subscription prices while reducing quotas on cheaper plans. GitHub Copilot has also shifted to an AI credit-based model. Newer models deliver better performance, but they eat up far more tokens and are significantly more expensive to run.
LLMs have a scaling problem. The operational cost of the underlying infrastructure is rising rapidly and fials tto meet growing demand.
Over the last eight years, I have realized that engineering is not just about writing code. It is about solving problems of every kind.
I currently work as a platform engineer, building and managing an integration platform. Before this, I worked mostly in full stack roles across backend and frontend systems. Surprisingly, I have grown far more in my current role than in any previous one.
What makes this role different is that the work is not limited to code.