The author of this piece, Juan Perez, is “a former global head of research at Morgan Stanley and former group head of research, data and analytics at UBS.”  Mr Perez begins with the piece with an analogy featuring Isaac Newton: “Sir Isaac Newton lost a fortune betting on the South Sea Co. Perhaps he did not take into account the “precautionary principle.” If you don’t understand something, even if others seem excited about it, it’s better to do something else or wait until you do get it….This principle should perhaps be borne in mind by investors and investment banks as artificial intelligence is developed for market and financial research…

Any operator will be able to create plausible comments with credible data. This might be considered by some as an alternative to the diminishing grip on the “official view of the future” that traditional financial intermediaries have today. A subscription to OpenAI and an X account will be all it takes to pour fuel on the fire of uncertainty. The total cost could be as low as US$30 a month for the soap box and the content generator.

But overconfidence in a potent AI tool will replicate the error of Sir Isaac. Even powerful models are influenced by context like humans. The Federal Reserve Bank of St. Louis compared the performance of inflation estimates between a pool of professional forecasters and GPT3, and found the AI tool had less bias and lower errors, but it was subject, like the professionals, to something akin to mood swings.”

So how should AI and more specifically, generative AI, be used by investors? Mr Perez makes several thought provoking points here: “…generative AI will bring benefits. It is a wonderful tool to test many alternative hypotheses at a very low cost. There will still be flaws that come from a backward-looking perspective, but if we look at a problem from many angles, the combined prediction error of the ensemble will be radically reduced.

It will be possible to use an AI lens to analyze Walt Disney Co.’s strategy, for example, looking at past reactions to factors such as price changes in streaming, success of blockbusters or how weather affects its amusement parks. All this can be done by large research outlets or multi-manager platforms. But it requires a major investment in infrastructure, diverse subject-matter experts and lots of communication within the firm. This is difficult to sustain.

The next ecosystem could be very different. On one hand, imaginative professionals will be able to prompt multiple inquiries at a fraction of the cost. But alternatively, large firms will be the ones able to afford highly specialized, built-for-purpose models to chase multiple sources of returns. This could be done by an alliance with a tech company. Or we may see firms uploading data to modify the generic AI models. These options appear likely as companies such as Open AI may create a platform for other businesses. It is hard to say which is the winning hand.”

Mr Perez concludes by saying that just as the previous generation of investment professionals got by thanks to Microsoft Excel, for the next generation “the value may lie in the proper interrogation of the AI tools. We can see that large firms may need to hire more engineers to fine-tune the process of interrogating the models. A discipline in its own right.”

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