A powerful, practical use of AI in investment research is to verify management's track record. By feeding all historical earnings call transcripts into a large language model, an analyst can quickly ask whether management's past promises and guidance materialized, automating a crucial but time-consuming due diligence step.

AI can be a powerful fraud detection tool by comparing a company's public statements against alternative data. For example, it can analyze satellite imagery of shipping traffic or factory activity and flag discrepancies with management's guidance.

For complex enterprise tasks, the latest AI models are often intelligent enough. The true challenge is the 'context gap'—engineering systems that can absorb, clean, and understand the vast, messy, domain-specific context of a single client, like 25 years of financial documents, to apply that intelligence effectively.

AI can quickly find data in financial reports but can't replicate an expert's ability to see crucial connections and second-order effects. This leads investors to a false sense of security, relying on a tool that provides information without the wisdom to interpret it correctly.

Many media outlets misapply AI by trying to make it write articles, which results in terrible quality. The technology's true journalistic value lies in its unprecedented ability to analyze vast troves of legal and financial documents, uncovering stories that were previously impossible for humans to find.

During the time crunch of earnings season, AI excels at synthesizing disparate information. It can instantly compare a CEO's positive guidance against the recently reported cash flow statements of multiple competitors, flagging potential overconfidence or a genuine outlier.

Instead of solely focusing on AI fallibility, a major application is using AI agents to audit human work. Perplexity's "Final Pass" feature analyzes documents for factual errors and internal inconsistencies, finding glaring mistakes in things like Gartner's earnings press releases and work done by professional accountants.

Beyond outright fraud, startups often misrepresent financial health in subtle ways. Common examples include classifying trial revenue as ARR or recognizing contracts that have "out for convenience" clauses. These gray-area distinctions can drastically inflate a company's perceived stability and mislead investors.

When building revenue models, AI can quickly analyze infinite data slices to spot outliers that skew metrics, such as zero-day service renewals or old opportunities creating survivorship bias. This leads to a more accurate model, representing a performance gain, not just an efficiency one.