Industry News #2: AI Regulation, Data Battles, and Scraping Trends

The AI and data industry is moving faster than ever. As a result, the rules are quickly changing. From the European Union’s push for AI transparency to major lawsuits, the way businesses gather, use, and protect data is under the spotlight.

For data professionals, proxy providers, and AI developers, these shifts are shaping how web scraping is done, how infrastructure is built, and how businesses can stay compliant without losing their competitive edge.

Let’s dive into this month’s biggest updates and what they mean for the future of data. If you prefer to watch, check out the video below:

1. Why EU’s AI Regulations Could Reshape Data Collection

The European Union is finalizing regulations that will force companies to disclose how exactly they train their AI systems. This will include data sources and design choices behind them.

On the surface, it sounds like a push for accountability. However, this shift puts scraped data under the spotlight. Businesses won’t just need technical infrastructure to collect it, they’ll need the documentation to prove where it came from and how it’s used.

This isn’t just a paperwork problem. It changes how proxy infrastructure is deployed in Europe, since every request may need to comply with jurisdiction-specific regulations. Transparency is no longer optional. Instead, it is becoming a part of the technical stack itself.

2. Meta, Google, and xAI Divided over EU Code of Practice

Meta has refused to sign the EU’s voluntary AI Code of Practice, while Google signed on despite reservations. Meanwhile, Musk’s xAI chose to back a separate EU AI safety initiative . The differing reactions from AI leaders suggest there’s a growing divide among the largest AI companies and the way they view regulation.

What does this rift mean for the data industry? Each approach could shape how companies handle scraping, licensing, and compliance. Depending on which ecosystem you operate in, you may find very different expectations for how data should be accessed and used.

3. Who Decides the Future of AI - Regulators or Big Tech?

According to the Guardian, Google, Meta, and Amazon are effectively steering the future of AI development on their own terms, with very little external control. They’re not only influencing the technology, but also the ethics and economics around it.

This focus of power raises a simple question. Will a handful of corporations get to decide how data can be gathered and applied? For infrastructure providers and data professionals, it means preparing for two possible futures. Either regulators will eventually impose strict governance, or these companies will keep setting their own rules.

One thing is certain - the ability to access data independently will remain critical.

4. AI Projects Are Crashing - And Poor Data Is to Blame

Nitesh Bansal, the CEO of R Systems, recently warned about the biggest obstacle to successful AI. It’s not infrastructure, model design, or performance, but poor data quality. Poorly parsed and incomplete data has been shown to derail machine learning projects, even with state-of-the-art models.

For enterprises, this shifts the focus toward governance as a critical component. Collecting data is not enough, it needs to be clean, traceable, and relevant to the use case. This is where ethical scraping and reliable proxy networks come in. They make it possible to build high-quality datasets that are consistent, transparent, and valuable over the long term.

5. Claude API Ban Shows Fragile AI Alliances

OpenAI lost access to the Claude API earlier this month after Anthropic claimed the company was violating its terms of service. Several reports suggest the decision was driven by both competitive strategy and different philosophies around responsible AI development.

This move reflects intensifying rivalry at the very top of the AI industry. Collaboration and sharing are being replaced by closed ecosystems, which raises the stakes for companies that do not own their infrastructure.

For data teams, the lesson is clear. Relying on competitors for access to models or datasets is a risky strategy, to say the least. Controlling your own data pipelines and scraping infrastructure ensures independence. This independence is a must in a landscape where partnerships are fragile.

6. Reddit vs. Anthropic: The Case That Could Redefine AI Data Collection

In a recent lawsuit that could change AI’s relationship with the web, Reddit has sued Anthropic, claiming that the company uses its vast library of user posts to train AI models without consent. According to the filing, Anthropic scraped data from Reddit discussions and used it to make Claude more conversational and knowledgeable.

If Reddit wins, AI developers may be forced into licensing agreements with content platforms before using their data for training. This could dramatically raise the cost of training and lock smaller players out of the market.

On the other hand, a win for Anthropic would re-emphasize the view that public data is fair game, at least until new laws say otherwise. This would offer AI companies the freedom to scrape data and train their models without needing permission.

Either way, this case has the potential to redefine the economics of web data almost overnight.

7. From Satellites to Social Feeds: The Data Gold Rush

The global alternative data market is on track for significant growth . It’s fueled by demand from finance, retail, and tech sectors. This includes everything from satellite imagery to e-commerce patterns and social sentiment scraped from the web.

The opportunity is massive, but so are the challenges. Data quality, privacy, and integration remain challenging problems to solve. Proxy networks and structured scraping pipelines are already at the core of this shift, making diverse datasets accessible and actionable for companies that need an edge.

8. Entity Resolution Errors: The Hidden Risk in AI

Entity resolution is the process of matching digital records to real people or organizations. If done well, it helps banks fight fraud, hospitals protect patients, and governments improve security. When done poorly , it can merge two identities into one or split a single person into multiple profiles, causing poor data quality, compliance issues, and business disruptions.

Since scraped datasets often feed into these systems, input quality and traceability are critical. Clean pipelines and proxies that preserve session integrity reduce mismatches and keep records accurate. In industries with strict compliance, this kind of precision is not just important, it’s mandatory.

9. Cloudflare Calls Out Perplexity for Abusive Scraping

Cloudflare has recently accused Perplexity AI of scraping websites without consent while disguising its traffic through AWS and other proxy services . The comparison used by Cloudflare was striking, saying that Perplexity behavior resembles tactics used by North Korean hackers.

Whether the claim holds up or not, it highlights a growing problem. The line between responsible and abusive scraping is becoming increasingly hard to draw. For companies that rely on data access, this is a reminder that transparency and ethical practices are key to staying out of the spotlight and maintaining long-term access.

10. The Walls Go Up: Media Tightens Rules on AI Access

News Group Newspapers Limited recently issued a notice warning about potential issues with automated access to their services. They cite copyright protection and lost revenue as the main drivers. This reminder is important for both those who use the content legitimately and those who may rely on automated methods unknowingly.

For data teams, this raises the friction of scraping media content, forcing a choice between technical workarounds or formal licensing. With publishers moving aggressively and content creators becoming more vigilant about how their material is used, the balance is shifting toward legal and business negotiations rather than pure technical access.

11. Scraping Wars or Data Deals? A Call for Collaboration

Not everyone believes scraping wars are the answer. Some industry voices argue that publishers should engage proactively with AI companies instead of fighting them. By getting a seat at the table and negotiating directly, publishers could shape fair compensation models and retain control over how their data is used in the AI-driven content landscape.

For AI developers, this could provide a clearer, more reliable path to the datasets they need. More importantly, collaboration could prove to be more sustainable than constant conflict.

Final Thoughts

The big picture is clear. AI and data are entering a phase defined by accountability, governance, and quality. Collecting data is not enough anymore. It has to be transparent, ethical, and managed properly.

At IPRoyal, we power the infrastructure that makes responsible, real-time access to online data possible. Whether you’re training AI models, gathering alternative data, or monitoring web content, our solutions ensure your workflows are fast, reliable, and most importantly, compliant.