The Cure for AI Hallucinations Isn’t a Bigger Model, It’s Fresher Data

AI has become very good at producing answers that sound fluent, confident, and complete. The problem is that confidence and correctness are not the same thing. That gap becomes most obvious when models are used in fast-changing environments. A system can summarize a topic or answer a question persuasively while relying on outdated, incomplete, or simply no longer true information. This is the essence of the hallucination problem: AI can sound right even when reality has already moved on. The answer to that problem is less glamorous than better reasoning. It is access to fresh, real-world data.

Training alone does not guarantee relevance

For a while, AI progress could be described almost entirely in terms of better models: stronger reasoning, longer context windows, faster inference. That still matters, but it no longer tells the whole story. Users do not just want fluent text anymore; they want answers that reflect the world as it is today, not as it was when training ended.

That expectation is why grounding has become such a central discussion point. Google describes grounding as a way to connect models to up-to-date public web knowledge rather than relying solely on model memory, and notes that web grounding can work alongside private enterprise data. This underscores that web data is not just training material. It is increasingly part of the runtime. For retrieval-augmented generation, assistants, and autonomous agents, fresh external data is what keeps systems relevant and mitigates hallucinations after deployment.

The shift is not theoretical, as Google rolled out AI-generated search summaries in the US, reinforcing the idea that discovery is changing shape. Search is no longer only about listing links. It is increasingly about systems that synthesize the live web into usable answers.

Fresh data is an infrastructure problem

Public web data may be available in theory, but making it usable for AI requires infrastructure. Data has to be collected reliably, refreshed quickly, structured into usable outputs, and delivered in a form that downstream systems can actually use. Hence, this raises the bar for the infrastructure underneath. At GTC 2025, NVIDIA unveiled its AI Data Platform, a reference design that storage and cloud leaders are using to build a new class of enterprise AI infrastructure for always-on indexing, multimodal retrieval, and lower-latency agentic workflows.

It is a clear signal of where the market is heading: the model is no longer expected to work on its own. It is one part of a larger system that must remain connected to a changing world. And that world is getting bigger fast—the global multimodal AI market is projected to grow at roughly a 38% CAGR, reaching tens of billions of dollars by 2030, with much of that value depending on accurate, diverse, and constantly refreshed data.

Working on web data infrastructure at Oxylabs, I see this play out across multiple use cases simultaneously. Sometimes the demands of AI projects are unlike anything encountered in the market before, and require solutions that have not existed before to ensure the tools work well in real-world conditions.

Agents need reliable web access

AI agents are often discussed in terms of what they can do, but a more practical question is whether they can reliably access the web at scale, on JavaScript-heavy or otherwise difficult sites. This often calls for browser-based infrastructure rather than lighter methods.

The issue is illustrated by early results of Oxylabs’ Web Openness Index, which evaluates various metrics of web accessibility across more than 120 countries. Practical reachability, which measures how effectively websites respond to standard automated HTTP requests, averages 83.4 out of 100. Anti-automation friction, scores tracking barriers, like CAPTCHAs, rate limiting, and fingerprinting where lower numbers indicate higher resistance, averages 62.8. Meanwhile, structured data interoperability, which assesses the availability of machine-readable data, drops further to 60.3.

What this boils down to is that, globally, there is a gap between websites’ willingness to share data with AI and other automated tools and the difficulty of actually accessing that data. In practice, this means that we need infrastructure that adapts and overcomes the structural deficiencies of the web.

There are a few solutions for this. One is a headless browser—supporting automation and extraction on difficult targets where rendering and anti-bot handling matter. It facilitates data collection via organic-like browsing and seamlessly deals with many forms of friction, such as CAPTCHA.

Our own headless browser comes with a twist. While it’s called headless, following standard industry naming, on our infrastructure, it is actually headful. The distinction matters because websites can often recognize a true headless browser and block it, incorrectly suspecting wrongful activity. A browser for scalable data access that nonetheless acts like a normal browser can be indispensable for contemporary AI search tools that provide instant answers, ensuring those answers are truthful rather than hallucinated.

Ultimately, ensuring that AI tools are grounded in reality as much as in internal enterprise data, we need to constantly listen to AI developers as their demands grow and change. Just to stay at the same level, you need constant innovation to adapt to the changing conditions. But, usually, we need to go ever bigger.

A stronger bridge between models and the world

The next generation of AI will be shaped not only by model progress but by the systems around those models. If we want AI that does not hallucinate, we have to think about how it connects to reality: the infrastructure that connects models to fresh public web data, balances scale with speed, and remains dependable as the web changes. A powerful model with a reliable bridge to current reality is much closer to what users actually expect and could trust.

That is the space I will explore in my presentation at the AI Engineer World’s Fair. The conversation around AI often starts with intelligence in the abstract. I am more interested in what keeps that intelligence useful in practice. Because if AI is expected to operate in the real world, being grounded in fresh data is not an option – it’s a top requirement.