FROM THE BLOG

These days, all roads seem to lead to artificial intelligence — how it’s changing industries, decision-making, and maybe the world itself. With S&P 500 companies projected to spend ~$1 trillion per year on AI, it’s no wonder investors are paying attention. But for many, AI still feels more like magic than machinery.

I’ll offer a hot take for those seeking to demystify the AI topic with clients: what if we’ve been using the original form of AI all along?

If you want to see how millions of people contribute information, make decisions, and create something useful, long before the first chatbot we had the market.

From Buttonwood to Bots: NYSE as the “OG” AI
Back in 1792, a handful of brokers gathered under a buttonwood tree on Wall Street and agreed to trade only among themselves under shared rules. That was the start of what would become the New York Stock Exchange.

What had been a chaotic, opaque mess of deals became a structured marketplace. Scattered human decisions became a formal system of price discovery.

Markets Think — They Just Answer in Prices
A market price is more than just a number on a screen. It’s the result of constant negotiation between buyers (demand) and sellers (supply). Imbalances between these parties cause price changes:

• If there are more buyers than sellers willing to sell at a certain price, the price must rise until enough sellers are willing to part with the asset.
• If there are more sellers than buyers willing to buy at a certain price, the price must fall until buyers see enough value to step in.

It’s a self-regulating system and incredibly efficient at digesting information. News, opinions, expectations, and emotions all show up in the price. No one person sets it, but together, all market participants do.

AI Works the Same Way — But Answers in Words
Generative AI likely had its “buttonwood moment” in 2022, when tools like ChatGPT became available to the public. AI moved from research labs with limited participants to something anyone could interact with. Suddenly, a messy tangle of data was turned into structured, conversational output. People could ask questions, explore ideas, even draft legal agreements, all through an interface trained on the collective work of humanity.

These massive volumes of data — books, websites, conversations — are used to predict the most likely next useful word or idea based on patterns it has seen before. It doesn’t “know” facts in the traditional sense. But if a concept shows up in a lot of places and is often connected to certain ideas, it’s more likely to show up in a response.

You can think of this like supply and demand for ideas:

• The more “buyers” a concept has (i.e., it appears often, is reinforced across sources), the more likely it is to surface as a response.
• Less common or outlier concepts are like a thinly traded stock — still there, but unlikely to be viewed as relevant until conditions change.

So, just as market prices change based on aggregated trades, AI models generate outputs based on aggregated patterns. As with capital markets, where more adoption leads to better price discovery, greater liquidity, and less potential manipulation, AI platforms also benefit from increased engagement and feedback.

Why This Matters to You
If markets are trading floors for capital, AI is becoming the trading floor for ideas.

Your clients generally understand how markets work and might benefit from a similar approach to AI: curiosity, discipline, and a long view. There will be winners and losers, just as there have been in the equity markets since 1792.  However, the prevailing trend of US market prices since that time can surely be illustrated as going “from the lower left to the upper right” – and we are all the better for it. With your help, clients can see AI less as sorcery and more like just the next way to harness human intelligence at scale.

Later,

Paul Keeton
Managing Director, Investments & Advisory Solutions

Posted by Prospera Financial