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.
It is not far off to say that coding agents have been the most visible and widely adopted AI product to date.
Agent offerings like Claude Code, Codex and GitHub Copilot are already used across many enterprises.
On the other hand, we do not see the same level of success in other white collar domains or in day to day life. We talk about agents doing everything, but very little work has actually been handed off in areas like finance, legal or healthcare.