_____ _______ _______ _______ _______ _______ _______ / \| _ || _ || _ || || _ || _ | | || ||| | | || |_| || | | ||_ _|| |_| || | | | | || ||| |_| || || |_| | | | | || |_| | | || ||| || || | | | | || | |__|__| |_______||_______||_______| |___| |_______||_______|
In 2026, I have hardly produced any code that was not generated by AI. Models like Claude Opus 4.8 and GPT 5.x, along with agent harnesses like Codex and Claude Code, made it extremely easy to generate production code. So much so that people started throwing around ideas like “coding is a solved problem” and “code is cheap.” I disagree. I think we have simply moved a level of abstraction up.
Read article →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.
Read article →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.
Read article →