AI Coding's $800 Vercel Bill: Review Fundamentals
Blind AI-assisted coding racks up surprise $800 Vercel bills from default high-cost configs; switch to elastic builds (0.3¢/min vs 12¢), disable concurrent deploys, and optimize times from 4min to seconds for sustainable shipping.
Slash Deployment Costs by Auditing AI Defaults
AI coding agents recommend Vercel defaults that maximize expense: turbo build machines at 12¢ per build minute (vs elastic's 0.3¢/min) and concurrent builds for rapid deploys. Deploying dozens of times daily with overlaps led to an $800 bill in two weeks. Fixes include switching to elastic/standard tiers for small projects, disabling on-demand concurrent builds to queue sequentially (cancel prior ones mid-process), and using GitHub Actions hooks for builds while Vercel handles only deploys. These cut per-build time from 3-4 minutes to seconds, dropping weekly costs from hundreds to dollars. Builds slow from unoptimized processes compound per-minute charges—treat slow builds as the real culprit, not just frequency.
Blind Code Acceptance Creates Service Dependencies and Blind Spots
Coding agents like Cursor and Claude push services (Vercel, Resend, Fly.io, Railway) without evaluating fit, uptime, support, or plans. Resend hit 2M users in months partly from AI recommendations, signaling GEO (generative engine optimization) where top AI results drive growth. Skip platform risk assessment at scale: low-stakes vibe coding tolerates it, but production demands scrutiny. Anthropic ships 13 features/products in April's first two weeks (nearly daily) without manual code review—Boris Cherny (Anthropic) and Peter Steinberger (OpenClaw) confirm handoffs to AI post-Claude 3.5. Tools de-emphasize code: Cursor's new UI prioritizes browser previews over files, showing changes as line counts/deletes; review requires clicks.
Fundamentals Persist Despite AI Abstractions and Future Risks
Not reviewing AI code is intentional—industry shifts from tab-complete IDEs to chat-first interfaces obscure lines for speed. Natural language specs mismatch deployed functionality (unexpected features appear), and volume makes line-by-line impossible. Counter abstraction argument: prior layers (binary to Python) stayed human-readable; AI excels at code, so it may invent AI-optimized languages incomprehensible to humans, explained fuzzily in NL. Ship more (months to days) but understand less—vibe coders without basics face anxiety. Solution: learn core tradeoffs, configs, and patterns; AI accelerates but doesn't replace oversight for production.