Andrej Karpathy: "90% of your AI coding bill is paying for context you didn't need to send" Here are 10 things senior AI engineers stopped wasting tokens on: 1. Auto-context loading 50 files for a 30-line fix: $1.20/turn for tokens you'll never read. 80% input waste, every session 2. Running Opus on lint, format, and rename tasks: $0.60 for what Haiku nails at $0.02. 30x overpay on the cleanup tier 3. Tool call loops that re-send the full repo on every retry: 5x context cost per agentic flow. fixing these alone cuts 30-50% of bills 4. Sonnet as the default model: Kimi 2.6 matches its quality on most coding tasks at 1/6 the cost. defaulting to Sonnet in 2026 is leaving 60-70% on the table 5. Streaming responses on stable-prefix workflows: kills your prompt cache. you pay 10x for tokens that should have cost cents 6. "Just in case" file includes: 80,000-token prompts that should be 3,000. context bloat is the silent budget killer 7. Per-session knowledge rebuilding: 10 min writing a SKILL.md once vs paying agents to re-figure out your environment every run. $4 vs $0.30 per execution 8. Single-model setups: premium tier on every task is the most expensive mistake in AI coding right now 9. Asking 10 small questions one at a time: 10 separate input prefix charges vs one batched call. 70-90% savings on routine workflows 10. Buying Claude Pro + ChatGPT Plus + Cursor Pro: you seriously use one. the other two are habit, not utility what actually compounds instead: - context discipline (grep before fetching, always) - prompt caching on every stable prefix - multi-model routing (Kimi 2.6 default, Opus for the 10%) - graduated skills via SKILL.md files - profiling tool calls before optimizing prompts - the routing mindset (right model for right task) in 12 months, the gap between developers shipping on $200/month and $4,000/month budgets won't be skill it'll be how well they route study this.