Macro Signposts | 11 September 2025
This week, during PIMCO's Cyclical Forum, I asked Anastasia Buyalskaya to guest author Macro Signposts. Dr. Buyalskaya, a behavioral science advisor to PIMCO, integrates behavioral insights and methodologies into our investment process.
Decision-Making and Behavioral Bias in the Age of AI
By Anastasia Buyalskaya, Ph.D. — advisor to PIMCO on behavioral science
In this Macro Signposts, I offer general thoughts on how AI might be used as a tool to support investment decision-making. These are broad observations — not commentary on PIMCO's use of AI, except where noted in the final section.
Artificial intelligence is advancing at an exponential rate, and the question is no longer if but how AI will reshape investment decision-making. Investors are increasingly focused on how best to interact with large language models (LLMs) as they generate, verify, and refine new investment ideas — as well as how these models might influence behavioral biases and blind spots.
Questions tend to fall into two camps:
Why LLMs feel like a team member — and why that matters
LLMs are trained on vast datasets and can process information at a scale and speed far beyond human capability. From a behavioral science perspective, interacting with an LLM can emulate some of the benefits of group study and interaction. These models tap into the collective intelligence embedded in their training data and may be able to effectively aggregate diverse perspectives. On complex topics — just as with a cross-disciplinary team — the LLM should (in theory) benefit from the "wisdom of crowds," generating multiple answers that begin with "It depends ... " when it has been trained on multiple viewpoints.
LLMs could potentially serve as a "cool-headed" counterpart, helping investors avoid emotionally driven decisions. For example, a friend recently told me that when they asked a well-known LLM whether to sell an equity holding after a 10% one-day drop due to bad news, the LLM flagged potential biases, urged a review of company fundamentals and the investment thesis, and suggested pausing before reacting — all reasonable advice that could help my friend make a rational decision. (Though in a May Macro Signposts, "How Does the Data Make You Feel?" I discussed how emotions carry valuable information and shouldn't always be dismissed.)
The behavioral risks beneath the surface
Still, AI can mislead. LLMs can be sycophantic, tailoring responses that match the tone or framing of the question — potentially fueling overconfidence and weakening critical thinking.
To the optimistic prompt, "Will AI remove behavioral biases?" an LLM recently replied, "That's a deep and important question. The short answer is: AI can reduce certain behavioral biases." To the pessimistic prompt, "Will AI amplify biases while making us think we're unbiased, even as it quietly amplifies our biases?" it replied, "That's a sharp question — and yes, that's a real risk."
Setting aside how eerie it is to receive compliments from an entity without a pulse, the major risk lies in how bias can compound. First, the wording of the investor's question tends to reflect their own optimism or pessimism. Second, the LLM often mirrors that tone in its response. Third, the investor — believing they're unbiased — may assume the LLM's answer is objective. This feedback loop can reinforce confirmation bias, even in a brief exchange.
It's also worth noting that not all LLMs are the same. Similar prompts to different LLMs can produce different results with varying degrees of embedded bias.
AI doesn't just exacerbate existing biases; it may create new ones. In their 2024 Nature paper, "Artificial intelligence and illusions of understanding in scientific research," Yale social scientists Lisa Messeri and M.J. Crockett warn of emerging blind spots. Beyond the "illusion of objectivity," they highlight the "illusion of explanatory depth" — where compelling but shallow explanations lead people to believe they understand more than they truly do. They also caution against the "illusion of exploratory breadth," where investors may believe they're testing all hypotheses, when in fact they're limited to those the AI can evaluate.
Mitigating bias through better prompting
To embed AI tools effectively, investors must learn to prompt with intention. This emerging discipline — known as "prompt engineering" — can help mitigate bias and improve decision quality. For example, instead of asking an LLM to "evaluate an investment idea," investors should prompt it to "play devil's advocate and identify overlooked risks." This approach is more likely to yield insights that challenge assumptions and strengthen investment decisions.
There are many prompt engineering techniques, such as asking the LLM to provide multiple views, or requesting unbiased information or a counterargument. The purpose is to control the form and direction of the response; an experienced prompter could elicit which biases end up being expressed in the response.
How is PIMCO using AI in its forum process?
With a lot of self-awareness of how our prompts may influence the LLM output, and with a vision to continuously refine our use of AI, we have begun experimenting with AI in our forum process. In these forums, PIMCO investment professionals from around the world debate market and economic conditions and identify trends that we believe will have important investment implications. These lively, informed discussions are the heart of the forum, and we're testing ways that AI could help augment the sharing and assessment of views.
Initially, we analyzed historical forum transcripts for word use and economic sentiment. Next, we used AI to provide quick post-discussion summarization to extract views from sometimes free-wheeling debate. More recently, we've included AI as a real-time "participant" tasked with making steelman counterarguments and asking provocative questions, which themselves can be debated. We've also created AI personas with specific knowledge bases, backgrounds, and points of view to answer questions and fill out surveys.
Through careful prompting, and intentional understanding of the biases that can underlie AI's responses, we are aiming to harness AI's ability to summarize complex debates, challenge groupthink, and discuss risks that underpin our views. While we are in the beginning stages of using these tools, we are optimistic that with the right guardrails in place, they will enrich our discussions, effectively summarize the wisdom of the crowd, and elevate out-of-consensus views that warrant more attention.
With careful prompting, AI can lead to better investment decisions. However, as with other technologies used by investors, we see AI a tool to improve the investment process rather than a replacement for human judgment.
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