Web Scraping in 2026: Six Insights from Pierluigi of the Web Scraping Club

Web scraping has become easier to start but not necessarily easier to run dependably. Pierluigi Vinciguerra, a veteran scraper developer, newsletter founder and co-founder of the data marketplace Data Boutique, argues that AI and improved tools speed up coding and integration while increasingly sophisticated anti-bot defenses make access harder. Reliability still depends on engineering practices and data-quality checks. The right method depends on the job: scraping APIs suit collecting a few records across many sites, dedicated pipelines suit large, recurring datasets, and browser agents work best for interactive tasks involving sessions. AI can assist with scraper development, but using it directly for huge collections may be too costly.

Vinciguerra’s team uses documented standards and troubleshooting procedures to guide AI maintenance; preparing those procedures took months, and humans remain involved, especially for the most protected sites. A successful run can still omit most records, so teams should check totals, compare with past runs, investigate outliers, spot-check expected coverage and test deep pagination. He describes costs as shifting rather than simply falling: engineering time is lower for ordinary sites, while spending on tools and proxies rises, and cheaper collection can encourage more projects. For data sellers, easier extraction is moving small, targeted requests toward AI tools, while demand remains for complete, refreshed datasets that require broad coverage and validation.