Inside the Pod: How to Use AI to Be a Smarter Investor

Thank you to everyone who is watching or listening to my podcast, How Do You Use ChatGPT? If you want to see a collection of all of the prompts and responses in one place, Every contributor Rhea Purohit is breaking them down for you to replicate. Let us know in the comments if you find these guides useful. —Dan Shipper

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The best investment opportunities are found in moments of perceived crisis. But not every crisis will be a good investment.

In fact, identifying which moments of crisis are promising opportunities is an art. It’s the stuff that professional Wall Street traders grapple with every day—and something amateurs like us pore over, too, determined to beat the market.

In this episode of How Do You Use ChatGPT?, Dan Shipper and IA Ventures managing partner Jesse Beyroutey tell us how they outplayed Wall Street. Dan and Jesse invested in Nvidia in 2019 when the shares were trading at $33. They’re worth nearly $900 a piece today. It was the best trade of their lives. In this interview, Dan and Jesse walk us through how they used Google’s powerful LLM Gemini Pro 1.5 to try and top that.

To get them started, Dan opened an account on investment platform Robinhood and put in $1,000 as the principal amount. With 90 minutes on the clock, Dan and Jesse were off to the races.

This interview is a masterclass in how to use AI to refine your own investment thesis and make smarter financial decisions. I came away inspired—and more than a little tempted to test my luck and day trade!

First, we’ll give you Dan and Jesse’s prompts, followed by screenshots from Gemini and ChatGPT. My comments are peppered in using italics.

A page from “Beyroutey x Shipper Investments,” a Notion page which records why they thought Nvidia would be a good investment in 2019—even though the company’s stock price was down. Jesse says they fancied themselves as “distressed asset investors.”

All screenshots courtesy of How Do You Use ChatGPTand Figma/Every illustrations.Dan and Jesse invested in Nvidia because they believed that the company was temporarily underperforming on account of identifiable reasons recorded in the screenshot above. With the clock ticking, Jesse uses the stock analytics platform TradingView to apply search parameters that might mimic this criteria. He adjusts the filters to find companies with a market capitalization between $2 billion and $100 billion, a positive gross profit, and a downturn in performance over the prior six months.

 Dan and Jesse upload the filtered list of companies from TradingView to Google’s Gemini Pro 1.5, prompting the model as follows.Dan and Jesse: Hey, I’m trying to pick a stock, I want to find a tenbagger. Specifically, I'm trying to replicate a trade I did in 2019 where I bought Nvidia. It was down temporarily because of the trade war, and now it's a $2 trillion company. Here's a list of potential starting points for trades. Does anything pop out to you?

After heavily caveating its response, Gemini identifies three potential companies from the TradingView list. However, something else in its response catches Jesse’s attention—the model correctly surmised that high-growth companies usually have high valuations. Jesse notes that this approach might lead them astray from their investment thesis of finding an “artificially depressed” stock. He suggests starting over by asking Gemini to identify search parameters that represent their Nvidia investment thesis.