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Humans Still Outsmart ChatGPT in High Stakes Equity Research

3 min read

Author

Midas AI
August 18, 2025

AI is bearing down on white collar work. The artificial intelligence steamroller is already crushing jobs in banking, insurance, and telecommunications. Against that backdrop, equity analysis stands out. For now, the field looks safe. That safety is not accidental. Stock analysis draws on seminal pieces of research amassed over the past 50 years, and the discipline persists across a wide set of institutions and roles.

Why Equity Research Still Holds a Human Edge

The most visible benchmark for investors and operators is OpenAIs ChatGPT. It is the system many people test first when they ask whether machines can interpret markets. On that question, the current record is clear, people doing stock analysis can beat ChatGPT. That does not diminish the utility of large language models in broader finance tasks, but it does underscore a boundary. The human edge in equity research is not folklore. It is grounded in practice that has been refined over decades.

It also spans a large ecosystem. Human stock analysis lives inside investment banks and brokers, where coverage and models feed clients and internal decision makers. It is central to hedge funds and short sellers that search for mispricing and fraud. It informs proxy advisers who shape governance debates, and it is embedded in journalism that digs into companies and markets. If you are a founder or operator building for this domain, you are not selling into a single buyer with a single workflow. You are touching a network of professionals who rely on established research traditions and who assess tools by how they perform against those traditions.

The Shifting Benchmark for Analysts

That is why the headline story and the operational reality can both be true. Equity analysis looks safe for now, yet investors who rely only on traditional analyst models may be betting against the future without realizing it. The reason is not that machines have surpassed the best humans on stock calls. It is that the broader environment is shifting under everyones feet. When AI pressure is strong enough to upend parts of banking, insurance, and telecommunications, it will also change the expectations placed on research, the costs of producing it, and the speed at which insights move across the market. If you sit still, the benchmark you compete against will not.

A pragmatic way forward is to treat the existing record as the floor for evaluation. The body of seminal research over the past half century is the baseline. If people can beat general purpose systems like ChatGPT on stock analysis today, that tells you how to measure tools tomorrow. The bar is not novelty for its own sake. It is whether a system can be integrated into the daily work of analysts in investment banks, brokers, hedge funds, short selling shops, proxy advisory firms, and newsrooms, and whether it can match or improve the quality these groups already deliver. That is a higher standard than demo flair, and it is the right one.

The counterintuitive lesson is simple. The resilience of equity research today is not a reason to delay modernization. It is a reason to do it on your own terms. Start where the facts are strongest. Acknowledge that people can beat ChatGPT on stock analysis and that equity analysis is still a human stronghold. Then use that strength to define what good looks like when you evaluate or build AI systems. In a market where AI is altering adjacent functions, sticking to yesterdays models as the only answer is a risk you do not need to take.

Equity researchers are not being replaced in one stroke. They are being surrounded by change. The fields around them are already shifting, and the tools at hand are improving. That is why clinging to traditional models alone is a bet against the future. The safer move is to measure new systems against the best of the last 50 years and adopt what clears that bar.