Recent developments in Artificial Intelligence (AI) systems, especially agentic AIs, have shifted proxy server use and made it a leading area of innovation. Proxy providers are responding by doubling down on the utility of their products for AI applications.

AI’s Data Problem

AI models require massive amounts of diverse and continuously updated data to train. The technology of Large Language Models (LLMs) like ChatGPT is based on hundreds of billions of words drawn from the internet, books, and various other databases.

Experts have warned for some time now about running out of data to train LLMs, and solutions are being actively discussed. If AI tools are to solve more specific, practical problems, more and better-quality data are needed.

LLMs trained on the same general datasets are bound to generate similar results. One major trend for solving this problem is to narrow down to smaller, specialized models and AI tools.

Even smaller, self-hosted LLMs, which companies run privately on their own infrastructure, face a similar appetite for data. The newest and most promising innovation, agentic AI systems that can execute various tasks and make decisions in real time, raise the stakes even further.

Relying on historical training data has been found to be lacking. Rather, building a continuous live information feed has been discovered as a possible solution. Data quality is also important, as models trained only on data from one region, language, or point in time are limited.

Proxies and AI

Collecting data for purpose-specific AI training is possible due to the accessibility of web scraping. It’s the process of automatically collecting online data using bots that visit websites, crawl their content, and extract what’s needed. It has been the cat-and-mouse game of the internet for years.

Websites increase their defenses, only for the web scraping community to invent new bypasses. Proxy servers have been at the center of this battle since the very beginning. These intermediaries allow users to change their original IP addresses to avoid geographical restrictions, IP blocks, and limitations imposed by online resources.

Unsurprisingly, proxy servers integrate easily into web scraping software and represent the bulk of data collection expenses. Yet modern proxy networks are increasingly built not just for web scraping, but for collecting AI training data and supporting agentic AI specifically. This strategic shift is a conscious choice by major proxy providers.

Proxy Market response

A look into some of the major proxy providers shows that web scraping was a major proxy server use case long before the AI boom. We reached out to IPRoyal, a leading residential proxy provider, for insight into the market's response to growing AI data demand.

“We’ve been supplying users with specialized web scraping proxies as a core product since the very beginning. In addition, we took it as our mission to help users' data extraction efforts with guides, videos, and other educational content,” says Mindaugas Čaplinskas, Chief Executive Officer of IPRoyal.

The groundwork that’s been laid is a result of years of effort and could not have been done merely to meet the demands of AI data. The popularity of self-hosted LLMs, agentic AIs, and other tools fueled the need for quality web scraping further.

Offerings for API-first products and infrastructure built specifically for AI or data pipelines have skyrocketed. Unlike in other markets, these trends cannot be fully attributed to advertising campaigns. Proxies were used for automated data collection for a long time, affecting even fundamental business practices such as pricing strategy.

“One of the possible solutions to increased revenue without a significantly negative impact on consumer sentiment or costs could be automated data acquisition,” concludes IPRoyal Co-founder Karolis Toleikis in their 2025 research study on price sensitivity.

AI solutions are already shaping essential business processes, and data collection is a crucial part of it. Yet, the same is true for websites that want to protect their data assets. As websites started using AI-powered data protection, the proxy market responded with AI-driven data collection tools.

“Our newest AI-powered products are aimed at automating web scraping tasks so that our users can extract data with even fewer interruptions and manual work,” commented Mr. Čaplinskas on the direction of IPRoyal’s recent products.

Web unblockers and various APIs that automatically manage proxies and bypass website restrictions appear to be the new norm of data collection. As such, generating custom datasets for AI implementation and later training becomes accessible to everyone.

Of course, the biggest data sets are still in the hands of AI frontrunners, but proxy-powered data collection unlocks highly specified, locally trained tools for every user. It’s safe to assume that it’s the future proxy providers like IPRoyal are preparing for with their recent positioning towards AI trends.

Ethical and Legal Considerations

Data bottlenecks arise not only because quality data is scarce. In many cases, data collection can be unethical or even illegal, and much of the responsibility rests on proxy providers.

The proxy market is frequently shaken by scandals, such as the recent takedown of the IPIDEA network, where seemingly trustworthy providers were sourcing proxies from so-called botnets. Such networks of hijacked devices are using malware to be controlled remotely without the user's consent or knowledge.

In less radical cases, proxy infrastructure is sourced from software where the clause of device use for hosting proxies is buried behind legal jargon. Responsible providers are transparent about the process of IP sourcing, ensuring that IP addresses used in their pools come with consent.

Major providers take it as their responsibility to show transparency signals. Proxy sourcing policies, whitepapers, compliance standards, third-party audits, and various other measures have been the norm for a while now.

The other side of the issue is controlling how proxy IP addresses are used. Providers must enforce clear acceptable use policies and screen clients for abusive or unlawful scraping activity. Often, such requirements come from data protection, such as GDPR and CCPA, or similar regulations.

Using proxies, therefore, is not just a technical or financial decision – it's a matter of compliance. Running your AI data pipelines on non-complicit proxy infrastructure is bound to create legal and reputational problems.

Regulators worldwide are increasingly seeking to exert control over AI data collection practices. Proxy providers that balance high data protection standards against ever-increasing data collection demands will succeed.

Conclusion

While the newest AI tools are making headlines, the proxy networks working in the backline are quietly becoming a foundational layer for the AI infrastructure of tomorrow. Nobody knows the future, but the current positioning of major proxy providers suggests they have been preparing for such growth all along.