The Real Limitation of AI Scientist Agents: Data Access, Not Reasoning

AI scientist agents are often judged based on model quality and reasoning frameworks, but the real bottleneck appears to be the data they can access. This isn't about slapping a model on a GPU rental and calling it innovation. The intersection of AI and scientific decision-making shows that proprietary evidence can make or break an AI's capability.

The Experiment

Consider a controlled study involving a valuation agent tasked with drug-asset evaluation. Three versions of the agent were tested. Version A was a plain web-based LLM analyst. Version B had added structured tools and a 14-dimension valuation playbook, but it was Version C that incorporated the proprietary Noah AI corpus filled with curated pipeline, trial, and deal intelligence.

Across a 13-asset benchmark, Version B showed improvements in calibration and audit discipline, with accuracy rising from 0.80 to 0.89. Objectivity scores also inched up from 3.16 to 3.30. Yet, these gains didn't crack the factual ceiling. Versions A and B could only recover 0.25 and 0.38 of the curated gold competitive record, respectively. In contrast, Version C recovered a whopping 0.96.

Beyond the Numbers

It's easy to think that more sophisticated reasoning scaffolds could offset data limitations, but that assumption doesn't hold here. The truth is, proprietary data sets the upper limit for what an AI can achieve. Even a perfect non-proprietary report is capped at 3.83, a stark contrast to the 7.43 completeness-aware decision utility score that Version C reached.

So why should you care? Because it raises a pointed question: if the AI can hold a wallet, who writes the risk model? In an industry where data is currency, the agents with access to proprietary information hold the real power.

The Verdict

This isn't to say reasoning scaffolds are irrelevant. They do improve calibration and discipline, but the underlying data is what ultimately dictates decision quality. Decentralized compute sounds great until you benchmark the latency, and the same goes for AI models with limited data access. The vast majority of AI projects won't reach their full potential without proprietary data.

The takeaway is clear. For AI to make truly informed scientific decisions, access to proprietary evidence isn't just beneficial, it's essential. Show me the inference costs, then we'll talk about real-world application.