AI content isn't automatically good or bad for SEO. Google's position since February 2023 is clear: they don't care whether humans or machines wrote it, only whether it satisfies search intent and follows E-E-A-T principles (experience, expertise, authoritativeness, trust). The problem is that raw AI output often fails on experience and depth, two things that separate ranking content from page-three content. In practice, AI works best as a first-draft tool. We use it at Ottawa SEO to outline structure, generate meta variations, and speed up commodity content like product descriptions. But anything targeting competitive keywords gets heavy human editing—adding firsthand examples, cutting generic phrasing, injecting specifics that only someone in the industry would know. A 1,200-word AI draft might need 45 minutes of revision to become something that actually ranks and converts. The real risk isn't detection, it's sameness. AI models train on the same data, so they produce similar angles and phrasing. If you publish AI content verbatim, you're competing with thousands of near-identical articles. Google's helpful content system downgrades sites that feel mass-produced, and AI slop at scale is the fastest way to trigger that. What works: Use AI to beat the blank page, then edit like you're arguing with it. Add data from your own projects, link to specific tools you've tested, remove hedging language. If you can't tell whether a paragraph came from your site or a competitor's, rewrite it. We've seen AI-assisted content rank in position 1–3 for mid-competition keywords (DR 25–40 sites), but only after that editing pass. What doesn't: Publishing raw ChatGPT output, spinning existing articles through AI paraphrasers, or building entire sites on unedited generation. Google may not catch it today, but the March 2024 and August 2024 core updates hit AI content farms hardest. If you're scaling content with AI, budget 40–60% of the time you'd spend writing from scratch just for quality control.