AI for Investment Research: What Works, What Doesn't, and What's Coming
The actual edge isn't AI. It's the loop you build around it.
I started using ChatGPT for stock research about a year ago. My analyst friends were split between dismissing it as a toy and talking about it like it was going to replace them, and I wanted to find out which one was closer to right.
The honest answer, after a year of using these tools across several different models on real positions in my portfolio, is neither.
I moved from ChatGPT to Grok in the fall because Grok handled financial questions better. Then I moved to Claude when the more advanced model came out and the analysis got a noticeable jump in depth. I tried Perplexity along the way and wasn’t impressed. The tools have improved fast enough that anyone writing about them with confidence is going to look a little silly in six months, including me. So take this as a snapshot from May 2026 rather than a final word.
AI has made me a better investor, just not in any of the ways it was sold to me. The pitch from finance Twitter is that AI will democratize access to institutional-grade research and let retail investors find the next ten-bagger from their couch.
What AI is actually good at
When I was in equity research, the first week of covering a new company was almost entirely mechanical. You read the last four 10-Qs and 10-Ks, pull every press release for two years, listen to the last eight earnings calls, and build out a basic financial model alongside a rough competitor matrix. None of this required judgment. It required time, and it produced the substrate on which the actual thinking could happen.
AI does roughly that week of work in a couple of hours. You can have it read the last four quarters of a company’s filings and surface the major themes management has been emphasizing, pull a clean list of every competitor mentioned across those filings, or generate a serviceable version of the bull and bear case as presented by sell-side analysts who cover the name. None of these outputs is differentiated, but each one would have been a half-day of work ten years ago and is five minutes now.
Sentiment scanning is the other thing it handles well. What are people saying on the company subreddit, in the StockTwits feed, in the comments under recent YouTube earnings recaps? You can get a reasonable read in a few prompts. None of that is alpha by itself, but it’s data you couldn’t easily access before, and occasionally it surfaces something useful.
What I use it for most is getting oriented on complicated situations. A biotech running three trials at once with a patent dispute on top of it, or a roll-up that’s grown through 14 acquisitions in four years, can have a volume of disclosure that takes a full weekend just to read. AI gets me through that in an hour, and what would have been a Saturday is now the first hour of looking at a company.
What AI is bad at, and why it matters
The way you actually make money in individual stocks is by knowing or believing something about a company that the market has not yet figured out or accepted. That gap, between what the price reflects and what is actually true, is where alpha lives. Every big winner I’ve had, and every loser, came from a mismatch like that. The market eventually figured it out and the stock moved.
AI can’t find those mismatches.
Trained on public information, the model gives you back a tidy version of the consensus view. The bull case it constructs is the same one already reflected in the price, along with the bear case and the standard list of risks the sell-side has been flagging for months.
This is the opposite of what you need to find a mispriced stock. You need a non-consensus view, supported by reasoning the market hasn’t fully absorbed, and AI is structurally a consensus machine. It cannot give you an edge because the consensus view is, by definition, not an edge.
The other thing AI doesn’t do is hold a contrarian position under pressure. Some of the best calls I’ve made required sitting with a thesis for 18 months. The stock went nowhere, smart people told me I was wrong, and I kept asking myself whether I was the idiot. AI has no position to defend. It revises its view based on whatever’s in the context window. Ask it the same question three times with slightly different framings and you’ll get three different answers.
AI is bad at math. And not in a way that’s easy to catch.
It sets the problem up correctly and walks through the logic in the right order. Then somewhere in the middle of a DCF or a CAGR calculation, it drops a digit, and the final number is wrong. I’ve caught it doing this on dilution math, on growth rates, on weighted averages. The prose around the error is always confident, which is the part that makes it dangerous. I now run every number AI gives me through a calculator before I use it for anything.
The workflow that actually works
The way I use AI now isn’t to replace research. It’s to compress the time it takes to do the parts of research that don’t require judgment, so I have more time for the parts that do.
For any company I’m looking at seriously, I run a version of the same loop.
I ask one model for the strongest bull case it can construct, with specific numbers and timeline. Then I ask the same model for the bear case with the same level of specificity. I take both and feed them to a different model for critique, and then I feed the critiques back to the first model and ask what its strongest response would be. By the end of a couple of cycles I have a sharper picture than I started with, and more importantly, I have a sense of where the models are uncertain.
That sense of where the models are uncertain is the thing I’ve started to trust. If I ask three models for a real bear case on a stock and none of them can produce one that goes beyond generic risks, that’s a signal. The downside might actually be limited, because the bear case isn’t sitting in the public information set in any organized way. Conversely, if every model gives me the same coherent bull case in slightly different wording, I should assume that case is already priced in, and my edge has to come from somewhere else.
I don’t trust AI’s recommendations. I trust the texture of its disagreements with itself.
The other thing I do is calibrate its risk-return suggestions against my own profile. If you ask AI for “stocks with huge growth potential,” it will hand you a list of preclinical biotechs and pre-revenue lithium miners that could plausibly return 10x and are statistically much more likely to return zero. The model isn’t wrong, exactly. Those stocks do have huge growth potential. The model just has no way of knowing that “huge growth potential” for you means asymmetric upside with bounded downside, not lottery tickets. Risk and return are a personal question, and asking AI to optimize for them in isolation will produce a portfolio you should not own.
Where this is going
The biggest mistake individual investors are about to make is assuming AI will close the gap between them and institutions. It’s widening it.
Consumer AI gives you the equivalent of a smart junior analyst who reads filings fast. Inside hedge funds, AI is being integrated with proprietary data sets, real-time market feeds, and channel-check workflows built over decades. A hedge fund using AI on its own data is doing something different than you using AI to summarize a 10-K. The information asymmetry I wrote about in I Pre-Wrote My Research Reports Before the Earnings Call Even Happened has been augmented on both sides, but more on theirs than yours.
The floor has moved. Someone running a real research loop with these tools will make better stock decisions than someone picking based on whatever’s trending on FinTok. The ceiling hasn’t moved at all.
AI is a useful research associate and a useless portfolio manager. It can help you understand a company in a fraction of the time it used to take, but it won’t tell you whether to buy it, and any tool or prompt that claims otherwise is selling you something. The judgment is still yours. So is the conviction to sit on a thesis for 18 months while everyone tells you you’re wrong, which is where most of the returns actually come from.
What to Read Next
📖 Co-Intelligence by Ethan Mollick. The clearest book I’ve read on what AI actually does well, where it fails, and how to build a workflow that uses it without being misled by it. Mollick’s framing of AI as a “jagged frontier,” strong in some places and shockingly weak right next to them, maps almost perfectly onto what I’ve seen in investment research.
📖 Thinking in Bets by Annie Duke. The book that taught me to evaluate the quality of a decision separately from the quality of its outcome. Critical when you’re working with AI, because AI confidence is uncorrelated with AI accuracy, and you need a framework for assessing your own reasoning that doesn’t lean on whether the analysis sounded persuasive.
📖 The Most Important Thing by Howard Marks. Marks’ concept of second-level thinking, asking not just what is true but what is already priced in, is the single most useful frame I know for understanding why AI’s consensus output is a starting point and not an answer.
🎧 All three are excellent on Audible. The free trial gives you one credit to start. Co-Intelligence is the one I’d spend it on if you’re new to thinking carefully about how to use these tools.
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