There is a lot of enthusiasm around using AI to read filings, summarize earnings calls, and produce company primers. While those are parts of an analyst’s work, the investment process is trying to accomplish something more demanding: understand a business well enough to identify a mispricing and build conviction in that view.
The Fundamental Edge approach to AI for equity research starts with that objective. Getting up to speed or doing a “sniff test” can mean deciding whether an unfamiliar name deserves attention, refreshing a company you once knew well, or beginning a full research ramp. AI can help with those objectives provided you understand which one you are doing and what you need to know to make a decision.
Why is getting up to speed on a stock so important?
Getting up to speed helps you sustain idea velocity by qualifying promising names early on and disqualifying unsuitable ones before committing substantial research time.
It is easy to immediately dive deep when a stock looks inexpensive and you can see a path to a compelling return. But repeatedly spending days on ideas you wind up eventually killing for predictable reasons leaves less time to find investments that belong in the portfolio. You need to learn enough early on to judge whether this is the type of setup your PM would own.
In his Up to Speed webinar, Brett Caughran describes spending roughly 60 hours investigating Bayer and its proposed Monsanto acquisition. The initial attraction was a low pro forma earnings multiple and potential synergies. Further work raised concerns about Roundup litigation, regulatory hurdles, and management, and he passed.
The question Brett raises is whether those concerns could have been identified earlier in the process. While he avoided losing capital, he lost valuable research time that could have been deployed elsewhere. That is the reason to bring gating disqualifiers earlier in the getting up to speed process and use AI to help investigate them, while checking the evidence before rejecting a name.
How much research should an AI-assisted stock sniff test involve?
An AI-assisted “sniff test” should establish whether an idea warrants deeper work rather than balloon into a full initiation before you have committed to that decision.
Brett separates getting up to speed into different levels of commitment. You might want a quick rundown on an unfamiliar ticker to surface whether any part of the story is worth spending more research time. Getting current on a company you covered previously is another assignment, while fully ramping on a business you’ve never encountered requires substantially more work.
AI didn’t create these distinctions, but using AI for each of these tasks requires a clear bar for quality as well as a well-articulated stopping point An orientation can help you decide what to investigate, but it does not establish that you understand the company well enough to underwrite and eventually pitch it.
In Analyst Academy, Brett also emphasizes that sniff test criteria vary on your firm’s mandate and your PM’s style. The point is to identify only those actionable situations worth pursuing within those constraints, rather than apply the same generic screen to all portfolios.
How can AI help you get beyond a basic company summary?
AI can help you get beyond a company summary when you narrow the research on key drivers and investment debates you need to understand.
Brett’s June 2025 UnitedHealth research experiment illustrates the difference. He had covered healthcare services but had not remained current enough to know the name as well as he once did. His questions concerned how the situation had developed, how companies and regulators might respond, and what those responses could mean for future Medicare Advantage revenue and margins.
Those questions focused his research. He wanted to connect potential outcomes to what was embedded in the stock and identify developments that would help him assess the path forward.
Describing the reports, Brett wrote:
“with these reports I feel ready to start getting on the phone and doing my field research.”
He explicitly noted that he had years of industry context to draw on. That background of domain knowledge separates a seasoned investment professional from an early-career analyst who is using high-power AI tools, and that combination of discernment and pattern recognition is what backstops pushback on AI outputs. The reports helped him prepare for further diligence, and that existing knowledge guided his further assessment.
How do you avoid losing understanding when AI accelerates the research?
You preserve understanding by continuing to work through the underlying evidence and testing whether you can explain the business in a closed room without a computer, internet access, or the AI-generated report in front of you.
Brett describes investment insight as a mosaic formed through reading, conversations, financial analysis, and continuous attention to a company. You do not necessarily know which encounter with what material will produce an insight or nugget, which creates meaningful problems for analysts still developing their sensitivity to information.
In his March 2026 due diligence discussion, Brett questions whether bypassing original source reading by compressed summaries can short-circuit that process. A report may give you an unqualified conclusion without the experience of considering the details that support or complicate it.
His webinar’s standard is a closed-book test: can you explain how the business makes money and the nuances that move the stock? Underdeveloped comprehension leaves you vulnerable to the maelstrom of daily headlines and sellside reports because you lack the grounding to defend your findings independently. As you move toward a recommendation, AI-derived conclusions need to be checked against the original evidence.
How should analysts redeploy the time AI saves on stock research?
Analysts should use time saved on mechanical research to deepen diligence and comprehension, judging the result against what they could accomplish in the same working hours.
In the Up to Speed webinar, Brett asks:
“after 60 hours, what outcome gives me a more rigorous comprehensive understanding of the business?”
His illustration is that compressing some initial work could leave more time for primary research and deeper investigation. It is a framework to think about time allocation rather than a promise that every research assignment can be completed faster.
This is a more exacting standard than producing a company primer quickly. It requires understanding how the combination of your work and AI assistance better equips you to understand the key drivers, evaluate the investment debate, and revisit your priors as when new information contradicts your view.
Analyst Academy develops the investment process behind idea evaluation, business research, and thesis development, alongside instruction on applying AI within that process.
Analyst Academy instructors
Brett Caughran, Founder & Head Instructor
Brett brings 13 years of buyside experience at Maverick, D.E. Shaw, Citadel, Bell Rock (fund launch), Two Sigma and Schonfeld along with experience teaching FIN 494 “the hedge fund analyst process” at Arizona State University. He founded Fundamental Edge in 2022 to help redefine training on the buyside.
Andrew Carr, Analyst Academy Instructor
Andrew brings ~7 years of buyside experience at Balyasny, ExodusPoint, and Strycker View Capital, and years of experience coaching candidates to seats at single-manager, long-only and multi-manager firms. Having made a post-MBA career change and coached hundreds of analysts, Andrew thoroughly understands the challenge of the impact of targeted analyst guidance.