As large language model training, retrieval-augmented generation (RAG), and AI agent-based browsing become more common, both the scale and the nature of data collection have shifted noticeably. Compared to earlier rule-based traditional scraping, AI-related data collection — often called "AI Scraping" in the industry — places new demands on the underlying proxy network. This article looks at those shifts and what proxy providers need to offer to keep up.
Shift 1: Request Volume Is Growing by Orders of Magnitude
Traditional scraping tasks typically target specific sites or pages, keeping the scale relatively manageable. AI training data collection and multi-step agent browsing operate at a much larger scale — a single training dataset might need to cover thousands of source sites, and an AI agent completing a complex task can trigger a large volume of page visits in a short window. This raises the bar for how large a proxy network's IP pool needs to be; a small pool simply can't support this kind of high-concurrency, high-volume access.
Shift 2: Higher Demands on "Authenticity"
Many earlier scraping tasks tolerated lower-quality IPs — as long as the connection worked and data came through, that was enough. But as more websites deploy finer-grained risk controls, and detection of automated traffic keeps improving, using easily-flagged data center IPs for AI Scraping now often triggers a wave of CAPTCHAs or outright blocks, cutting collection efficiency significantly. Residential IPs, with network characteristics that closely resemble real users, have become the more suitable choice for this kind of work.
Shift 3: Regional Diversity Becomes a Requirement
Many AI use cases — training models for multiple languages and markets, or having an agent complete tasks that involve region-specific judgment — need collected data that itself carries regional diversity, covering the same type of information across different countries and language versions. That means a proxy network needs not just a large IP count, but coverage broad enough across countries and regions to meet this kind of diversity requirement.
Shift 4: Session Control Needs More Flexibility
When an AI agent runs a task, some scenarios need a relatively stable identity throughout (login states, multi-step interactions), while others need to switch perspectives frequently to gather a fuller picture. This means a proxy network needs to support both rotating and sticky sessions, so tasks can switch between them as needed rather than being locked into a single session mode.
Where 9HTTP Fits
9HTTP covers 200+ countries and regions worldwide with over 90 million real residential IPs, meeting the pool size and regional coverage that large-scale, multi-region data collection requires. It supports both rotating and sticky sessions, so teams can switch flexibly based on the task at hand, and offers a flexible, easy-to-use API that integrates directly with mainstream scraping frameworks and automation tools — making it straightforward to plug into an existing AI data collection pipeline.
A Note on Boundaries
Whether it's traditional scraping or AI Scraping, it's advisable to respect the target site's robots.txt and terms of use, and to keep request frequency reasonable to avoid placing excessive load on the target server. A proxy network solves the access-stability and regional-coverage problem — the collection activity itself still needs to stay within compliance boundaries.
Summary
The rise of AI Scraping has raised the bar for proxy networks on scale, authenticity, regional breadth, and session flexibility. 9HTTP provides high-quality residential proxy resources with global coverage, helping technical teams meet these new challenges in AI-related data collection.