If you have shipped a scraper, you know the failure mode: it runs clean for weeks, then a site pushes a redesign and every selector you wrote points at nothing.
That is the core fragility of traditional scraping. It binds to the page's exact structure, so the pipeline is only ever one markup change away from breaking.
LLM-based scraping takes a different route. Instead of matching selectors, it reads the rendered page the way a person would and returns the data by meaning. A renamed class or a moved div does not take it down, because it was never depending on that class in the first place.
The tradeoff is real. It is slower and it costs more per page than a hand-tuned script, so for a stable, high-volume target the traditional scraper still wins. The AI approach earns its keep on messy, frequently-changing sites, or when you are handling many different layouts at once.
The practical takeaway is to match the tool to the target rather than treating either one as universally better.
I put together a fuller breakdown of where each approach fits here: https://www.webbrowserbot.com/ai-web-scraping/
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