Letting an AI chatbot build your portfolio loads you up on major tech firms and earns you no real edge over passive index investing, a new working paper from the National Bureau of Economic Research has found.
In the study, the researchers asked different large language models (LLMs)—including ChatGPT 5.0, Grok 4.1 Fast, Claude Sonnet 4.5 and Gemini 2.5 Flash—to build two kinds of investment portfolios. One was passively managed, meaning the AI picked stocks and held them. The other was actively managed, meaning the AI could change its picks daily.
The resulting portfolios were aggressive and heavily concentrated in Big Tech, with Nvidia (NVDA) a near-universal pick. While many investors have been putting their money into these stocks, the NBER researchers noticed that LLMs were driven less by fundamental analysis than by the volume of media coverage.
According to the study, the AI models’ recommendations were primarily based on the amount of media coverage a company received, with recommended stocks having nearly 10 times as many news articles about them as the average Compustat company. (Compustat is an S&P database that collects information on public companies in the U.S.)
“The ability to grab attention within the universe of corporate news is a major driver of AI’s recommendations,” the researchers wrote.
The sites the LLMs used to gather information were primarily corporate websites, specifically those of semiconductor and other major tech companies. About two-thirds of the LLM visits were to company websites, with the remainder going to news services, government organizations and sites that provide stock analysis, the study said.
Semiconductor stocks made up an average of 41% of AI portfolios, about double their 21% share of the S&P 500 index, the NBER study found. That’s a problem for regular investors. Concentration like that could amplify losses if the sector turns, and the loudest names in the news cycle tend to be the ones that investors have already run up—it’s why they’re in the headlines—meaning you could be buying near the top.
And while the LLMs’ portfolios did yield higher returns than the market as a whole, beating the S&P 500, the researchers found that these returns were not outsized when accounting for trading costs or their bias toward tech stocks.
“AI takes risk, recommends a narrow set of assets, focuses on specific industries, and does not appear to exhibit better performance than passive characteristic-based benchmarks,” wrote the researchers.
On top of that concentration, the AI portfolios were built from unusually volatile stocks. Without any restrictions, the models picked stocks with an average market beta of 1.6, meaning the portfolios would swing about 60% more than the S&P 500 index in either direction. So, if the S&P dropped 10%, a portfolio like this would be expected to fall around 16%. Even when the researchers asked the models to match the S&P 500’s level of volatility, the portfolios still hugged the top of the allowed range, and sometimes broke through it.
Gemini provided the least diversified portfolios, holding a median of just 4.5 stocks, compared with 10 for Claude and Grok, the researchers said. And rather than diversifying further, the portfolios grew narrower as the models actively managed them over time.
The paper is preliminary, the researchers caution, and the Claude, Gemini and Grok results are based on only about four weeks of trading data. They also note that the prompts in the study reflect what a typical retail investor would type, not the more refined queries a professional might use to push the models harder.
