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The 10M-Token Context Window vs the $1M/Day Inference Bill: AI's Fundamental Economics Problem
Sora cost $15M/day to run. Lifetime revenue: $2.1M. Context windows keep growing. The economics that decide which AI products survive.
Sora cost $15M/day to run. Lifetime revenue: $2.1M. Context windows keep growing. The economics that decide which AI products survive.
SWE postings down 49% from peak. AI roles up 340%. Junior hiring collapsed 73%. The market is bifurcating and depth sets the price.
A $47K recursive loop went undetected for 11 days. MLOps can't monitor agents. The new operational stack for autonomous AI is emerging fast.
A rigorous RCT found AI coding tools slowed down experienced developers by 19%. The developers themselves believed they were 20% faster. The perception-reality gap changes everything.
Karpathy coined both terms a year apart. One builds $400M startups. The other lost Amazon 6.3 million orders. The difference is about to define which developers thrive.
Meta shipped 10M-token context. The model scores 15.6% at 128K tokens. Here's what actually works and what doesn't.
Every major open-source frontier model in 2026 uses MoE. A 120B model now fits on one H100. The self-hosting economics changed forever.
Alibaba's Qwen hit 1B+ downloads, beats GPT-5.2 on instruction following, and costs 13x less than Claude. The open-source AI race is over.
Microsoft launched MAI models built by 10-person teams that beat OpenAI's Whisper. The $13B partnership is fraying.
All three score ~57 on the Intelligence Index. Claude leads coding quality, Gemini leads math, GPT leads speed. Which to use when.