AI Eats Equity Research

What OpenAI’s New Deep Research Means for Equity Analysts

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“VisiCalc took 20 hours of work for some people and turned it out in 15 minutes and let them become much more creative.” — Dan Bricklin, co-creator of the first spreadsheet program

Some years ago I was at an investor conference in Macau. Those who’ve been to such events will be familiar with the set-up. Senior corporate executives take to the stage of a glitzy hotel ballroom to deliver slide presentations crafted by diligent investor-relations teams. Afterwards, they repair to the upper floors where the real action happens. Holed up in hotel suites, they receive rotating groups of investors eager to probe them on how business is performing. The biggest institutional investors get to meet management one-on-one; others share meetings with peers from different firms.

I was sharing my meeting with several others. I can’t even remember which company we were seeing. What I do remember is one participant who remained glued to his phone, yet still managed to ask the CEO remarkably insightful questions. His grasp of the numbers was exemplary – even as his thumbs flew across his screen, he fired back with detailed follow-ups.

I learned later that he’d been texting his research assistants in India. He literally had three interns in his pocket that meeting. I have no idea if it made him a better stock-picker, but it made for a more productive meeting, and helped make him appear informed (if a little rude) in the eyes of management.

Those interns are now redundant. They were disrupted by early iterations of generative AI. Now, that analyst can ask Perplexity for clarification of something the CEO says. Suppose he is meeting UBS after the bank reported earnings this week and management says something about their strong equity trading performance in the quarter (+44% year-over-year). Curious to see how that ranks with competitors, the analyst could perform a quick check: Better than Goldman Sachs (+32%), not as good as Morgan Stanley (+51%), according to Perplexity.

If he worked at JPMorgan Asset Management, he may choose to login to an in-house chatbot, LLM Suite, marketed internally as like a “research analyst that can offer information, solutions and advice on a topic.” Or he could use a finance specific application like Fintool, “a financial copilot for public equity investors.” A quick query on Fintool reveals that management had said on their earnings call that “while there continues to be broad-based positive sentiment around the market backdrop, global fee pools in January were off by more than 20% year-on-year,” so perhaps the strong performance in the fourth quarter of last year is not sustainable?

So far, the analyst might rest assured that while he’s been able to get rid of his interns, his own job is safe. The bots need prompting, they need to be told where to probe. But this is a field that is developing rapidly. This week, OpenAI released a new artificial intelligence tool called Deep Research, designed to carry out time-consuming research of the kind an analyst might undertake himself. Give it a task and after a few clarifying questions, it will trawl the public web to compile a comprehensive report, independently discovering and consolidating insights.

“Write me a research report about UBS's 4Q 2024 earnings,” I asked it. In five minutes, utilizing 19 sources, it gave me one: 3,500 words covering trends in investment banking revenues, how double leverage is hampering UBS’s ability to release capital to shareholders, and an update on the Credit Suisse integration following the bank’s takeover in 2023. The full report is available for subscribers over the paywall.

What does this mean for the future of research analysts? To explore what Deep Research means for equity research, read on.