Almost Timely News: 🗞️ Can AI Be An Investment Expert? (2026-08-02)

Almost Timely News: 🗞️ Can AI Be An Investment Expert? (2026-08-02) :: View in Browser

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What’s On My Mind: Can AI Be An Investment Expert?

Let’s head for the danger zone! (Cue Kenny Loggins, please) Someone asked in my reader survey if I would cover how to use generative AI for stock market analysis. Yes, you can, but there’s a whole host of warnings. So let’s get through all the warnings and disclaimers first.

Part 0: Warnings, Disclaimers, etc.

First and foremost, I am not a certified anything in finance. I hold no credentials and have no particular expertise in it. None of what I say in this newsletter is advice (in the legal sense of me telling you what to do) and the only advice you should take is from qualified, certified experts in the area. That is not even remotely me. My stock warning always applies: consult a qualified practitioner in your jurisdiction for advice for your specific situation.

Second, because it’s something that touches finance and investment, I have to disclose any conflicts of interest. I have retirement savings, mostly 401K-style investments, that are invested in no load index funds because even with AI, I’m not going to beat the market. I’m invested in an S&P 500 index fund, an international index fund, some money market funds, and a couple of bond and commodity funds that are also passive. I don’t buy or hold individual investments, nor do I recommend specific companies to invest in or short.

My biggest investment in an overall sense is as a co-owner of Trust Insights with my cofounder and CEO, Katie Robbert. Trust Insights does not currently have any financial management or investment clients; we do have clients that are publicly traded but we do not recommend investment for or against any client. You will see a paid product placement from my company, the Deep Research Suite, in this newsletter. I receive indirect financial benefit as an employee and owner if you make any purchases.

Third, while I may suggest some providers in this issue, I do not endorse any given data provider, nor am I compensated currently by any. My primary criteria for selecting any kind of financial data provider is whether the data is freely available or not because finance is not my area of expertise and I’m not going to pay for data I barely use, or use in an ad hoc manner. Likewise, if you follow the steps in this newsletter and use it to generate code, you do so at your own risk. AI frequently generates janky, sometimes hazardous code that you should never run without strong testing, QA, and validation.

Fourth and finally, nothing in finance and especially nothing in the stock market is ever guaranteed unless you’re doing something illegal, like insider trading. There are no sure bets, and I cannot promise any outcomes if you use the information in this newsletter. This issue is solely for educational purposes. Any ideas you implement are at your own risk, and there is always a real, meaningful chance that any investment you make can become utterly worthless or even a liability, sometimes in the blink of an eye. Proceed entirely at your own risk.

Part 1: Can AI Be An Investment Expert?

Before we dig into specifics, let’s tackle the big picture. Can AI act as a credible investment expert? If you go to places like Reddit or Xitter (my favorite neonym for the platform formerly known as Twitter, combining X and Twitter and pronouncing it in Hanyu Pinyin style as Shitter), you will see endless posts of investment bros touting how AI just made them a gazillion dollars.

Basic logic would suggest that if AI in fact did so, they would not be hanging out on Reddit. They’d be on their private island in the Maldives with their gazillion dollars. Most of the time, if you dig deeper, you find out that they’re actually selling a book/course/secret method for supposedly making a gazillion dollars on the stock market with AI but they themselves are not gazillionaires (which reduces their credibility considerably).

Today’s generative AI models have encyclopedic knowledge of the investment world, having consumed a lot of data about it, but… much of that data is mashed up. Remember that generative AI models have no understanding of facts, no ground truth (Claude’s favorite term) at all. They’re made of probabilities, and if they consume enough garbage, the probabilities around that garbage will exceed those of factual truth.

And the internet is filled with snake oil salesmen (and has been since the public was first allowed on it) promising get rich quick schemes - which the machines have also learned. So to start, the investment knowledge AI has broadly is untrustworthy, the foundational knowledge is shaky because of the sheer quantity of unhelpful, conflicting, or shady information on the internet about investments.

Second, generative AI models - even today’s state of the art models that every broligarch is touting as sentient Skynetesque machines that can hack into anything - also still can’t count. When you take the base model and have it do math, even the best models fail at it, especially if the math is complex - and the math around the stock market is super complex.

For anything involving math of this level, you need classical AI - machine learning and statistics, usually run inside some kind of code like Python, Scala, Julia, R, etc. Generative AI simply isn’t up to the task of doing this kind of math. Now, some generative tools have things like code interpreters (the ability to run limited amounts of code) right in chat, but those are often underpowered and have significant limitations, especially around data storage.

Third, generative AI models around anything stock-related have two serious data quality issues. The first is hallucination; AI tools are notorious for hallucinating in general, but when you’re dealing with probabilities that have very high frequencies - like, say, prices around stock symbols - the risk is even higher.

Then there’s staleness. All AI models natively have a training data cutoff date; their knowledge ends after a certain point. For example, Google Gemini’s 3.6 Flash model has a cutoff date of January 2025; nothing that happened after that date is in the model’s inherent knowledge. Claude Opus 5 has a cutoff of May 2026. That stale knowledge can have material impacts on investment strategy, for what I assume are obvious reasons. For example, investment strategies that worked during the pandemic are inapplicable now, but that core knowledge is part of what AI has trained on.

The last caution is a human caution. Humans as a rule tend to be incurious, especially with using AI. For example, I have a friend who has a habit of asking AI to do things like “give me a simple answer” or “give me just the right answer”, which when it comes to investments and stocks is an incredibly dangerous practice. AI will always give you an answer. It may not be right, nor will it be thoughtful. At a minimum, we always want to ask AI for multiple answers, and ideally we have thoughtful, critical, and reflective conversations as part of developing answers.

I’ve said for some time now in my keynotes and workshops that the three most important skills in AI today are creative thinking, critical thinking, and contextual thinking; nowhere does this apply more than in high risk applications like finance and investments.

What we derive from all these cautions is three key principles for using AI in a stock and investment context: