• AI
• Data collection

Key web scraping trends for 2025

Contents of article:

1. AI domination trend
2. Main positive trend. Automated web scraping agents
3. Web scraping difficulties within AI trends

Giving access to a pool of whitelisted geo targeted proxies, Dexodata knows how web scraping works in 2025. 1 million ethically sourced IPs, dispersed across 100 countries, means one contacts and utilizes our proxy service globally. Based on what people need from rotating residential IPs, dynamic mobile network proxies, datacenter offerings, etc. in terms of data collection, we summarize the chief web scraping trend as follows: making the most of enormous generative artificial intelligence potential coupled with tackling its shortcomings, pitfalls, constraints.

AI domination trend

By Forbes, AI market niches will hit $407 billion around 2027 (the annual growth rate is 37% per year). Rationales behind such investments in AI are plain, as 64% of entities anticipate this technology boosts productivity. When web scraping gets involved, adoption tempos might even be quicker. Data harvesting at scale is by definition a field ever-ripe for smart automation. 

Main positive trend. Automated web scraping agents

Soon, we expect to see totally automated AI agents, handling tasks end-to-end. These agents will both gather data, building upon rotating geo targeted proxies, and process and clean datasets swiftly, presenting them in comprehensible fashions. They will also conduct analysis and provide insights, reducing and eliminating the need for human intervention. This shift will see companies relying more on machine-like web scrapers.

Web scraping difficulties within AI trends

Each medal has a reverse side. AI in web scraping is no exception. Dexodata’s shortlist of challenges to overcome encompasses:

1. Trend # 1. Greater data generation by AI. Shortly, the total accumulated amount of data generated by AI will surpass that added by humans. In 2025, AI accounts for 90% of online content being published. This shift raises another critical issue, i.e., distinguishing between AI-created and human-crafted data. This is an ongoing research question concerning web scraping. No one can give ultimate questions. But it is better to keep this shortcoming in mind, tuning data cleaning, analysis, storage flows accordingly.
2. Trend # 2. Caution with transparency in web scraping. With the risks inherent in AI, it is imperative to establish robust guardrails to prevent intelligent systems from running amok. The autonomous properties of AI agents, which execute scraping tasks relentlessly, can be double-edged swords. While efficiency and productivity are primary goals, the potential for AI to act unpredictably through geo targeted proxies is a cause for concern.
Controllability is of increased relevance when it comes to legality and clearness in web scraping, for instance. Each stage of decision-making, from data harvesting, based on mobile network proxies or residential IPs, to output, must be lucid, documented, substantiated by guidelines, regulations, norms. This ensures compliance, building trust with users and regulatory bodies.
3. Trend # 3. Simple vs complex AI models for web scraping. The tension between straightforward, simple methods and complex decision-making techniques is a topic of significant debate. As we move towards more complex models like LLMs, attribution becomes difficult. This shift will reveal interesting regulatory developments, with web scraping purposes versus limits playing a central role.

As web scraping teams navigate emerging landscapes, it is essential to maintain a balance between leveraging advanced technologies and ensuring ethical, transparent practices. Doubly-so when sensitive data, to be assessed through proxies for social networks or mobile network proxies for business directories, is at play. As a KYC/AML-compliant and AI-driven service of geo targeted proxies, Dexodata is ready to help undergo inevitable far-reaching transformations.