AI can help stabilize the process of buying and selling financial assets.

Oct 19
A financial crisis brought on by AI, in Gary Gensler's opinion, is all but inevitable. To avoid such dreadful scenarios, he has urged for new regulations governing AI, along with many other regulators.

But these worries are largely overstated. On the web, AI is likely to lessen the likelihood of a market crash. After all, many events, some utterly random or unforeseen, have caused market downturns.
One worry is that a few underlying AI models may induce investors to act in a herd-like manner, with many of them purchasing or selling at the same moment because their models instructed them to.

The quantity of base models, however, is more likely to rise over time than fall. There will be diversity rather than consistency in the market because AI is undergoing a time of enormous innovation, with numerous startups being founded and numerous novel trading and investment strategies being developed.

A trading firm has an incentive to avoid using the same model as everyone else because doing so can cause them to sell during market panics or buy during momentarily rising prices, which is the exact opposite of what they should do.

Instead, the top brand will work to create models that are superior to those of its rivals. If a business learns that rivals are reliably employing a similar model, it can find flaws in the model and compete with those businesses.

One reason why regulation is not best equipped to manage potential over-centralization is that regulation tends to decrease rather than raise the amount and variety of technologies and programs on the market.

Artificial intelligence—and more broadly, quantitative techniques—are nothing new on Wall Street. It is not immediately clear how recent developments in massive language models will fundamentally alter the environment of the securities markets.

Stock price volatility has been low recently despite all the quantitative analytical methods used on Wall Street, and some of the volatility probably had more to do with the epidemic and its aftermath than with trading strategies or quantitative analysis.

The likelihood that AI will increase productivity and, hence, boost stock prices is one piece of good news. Bull markets are typically less erratic than bear markets, and even when they are, investors may find it easier to weather the storm since they have already made money.

Regulation becomes more challenging since software plays an independent and active role in market outcomes. Even to insiders, software is not always apparent to outsiders.
It is challenging to predict whether a given piece of software will perform as intended; if this raises concerns, the best course of action would be to raise capital requirements so that market participants have more protection if something goes wrong.

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