The Road to AI Trading Began Here
Ticker tapes and green-glowing Quotron computers reshaped Wall Street as the cutting-edge technologies of their day. Now comes the age of artificial intelligence.
Take a moment’s break from your financial robo-advisor and consider the case of Joaquin Miller.
Known as the “poet of the Sierras,” Miller (1837-1913) took part in the California gold rush, rustled horses, and fought as a mercenary in Nicaragua. Yet nothing prepared him for a confrontation with Wall Street’s breakthrough new invention—the stock ticker.
This “little wheel of fortune,” as Miller called it in an Oct. 30, 1880, column in the Chicago Tribune, turned any broker’s office into a miniature stock exchange.
“When I tell you that there are more than 5,000 of these ‘tickers’…you can form some idea of the magnitude of the business,” he wrote. “If we give 10 men to each ‘ticker,’ you have the spectacle of 50,000 stalwart men standing there holding up a little dotted string, waiting, hollow-eyed and anxious on the smiles of fickle Fortune.”
Today, instead of ticker tape, millions of stalwart men and women stare hollow-eyed into their phones to follow their investments. And when they aren’t online, artificial intelligence is still on the job.
Since May 17, 1792, when 24 brokers signed the Buttonwood Agreement, the New York Stock Exchange’s founding document, Wall Street has heartily embraced new technologies.
Wall Street hitched a ride on the railroads and filled telegraph wires with its ticker symbols. It discovered millions of new investors online, and developed computer programs that execute trades no human can match or, sometimes, even understand.
It’s part of a technologically driven competition that has helped expand markets and accelerate the flow of money and data to previously unimagined levels.
AI, with its robo-advisors and humanlike abilities to learn and grow, is just the latest advancement that threatens to turn Wall Street on its head—and make it much richer and more powerful.
Originally, the need for speed on Wall Street was filled by swift-footed lads. These “runners”—would-be investment bankers—raced between brokers’ offices and the exchange, delivering orders and returning with the latest quotes. The best might be promoted to office boy. It’s a tough way to break into the business—something today’s junior analysts, working 100-hour weeks preparing pitchbooks, might appreciate.
Wall Street profited from investment in the Erie Canal (1825), an engineering marvel that opened the Great Lakes to the Hudson River and the Atlantic. Then, in 1845, Samuel Morse began stringing telegraph lines from New York toward Philadelphia, Boston, and the Mississippi. Wall Street’s prices quickly supplanted the local quotes, which could vary from city to city, becoming the national standard.
As telegraph lines buzzed with quotes, locomotives chugging alongside carried the gold and certificates needed to complete trades in a fraction of former times. A nationwide financial system was developing.
Thomas Edison’s first practical stock ticker went into general use in the 1870s. Joaquin Miller, researching an article to be titled, “A Week in Wall Street,” soon found himself jostling around a ticker with a rabid group of speculators.
“Ten, 15, or 20 at a time would clutch at the tape, as it streamed out with its endless lines of quotations, and mutter to themselves, jabber at each other, swear like pirates, drop the tape, and dash away” to place orders, he wrote. The orders were now made by telephone, another recent invention, ending the heyday of Wall Street’s runners.
The broker’s office, powered by ticker and telephone, wouldn’t change much for a century. When it did, the moment was captured in Hollywood’s 1987 blockbuster Wall Street. The star is Michael Douglas, whose corporate-raider character Gordon Gekko memorably declares, “Greed, for lack of a better word, is good.”
The co-stars, it can be argued, are the offices, jam-packed with high-tech equipment and everything bathed in the green glow of computer screens. At the time, it looked futuristic.
The machines were real, made by Quotron Systems, and by 1986 there were 80,000 leased terminals across Wall Street. It was basically a high-powered brain that helped analysts crunch numbers at their own desks, beginning the era of Big Data. Fittingly, by the time Wall Street hit theaters, Citicorp had acquired Quotron in a hostile, $680 million takeover.
If the ticker brought investing into every broker’s office, Nasdaq, founded in 1971, brought it to every PC.
For years, Nasdaq served as the NYSE’s high-tech little brother, until the dot-com boom of the late 1990s—powered in part by online platforms like E*Trade—turned start-ups like Intel and Amazon.com into market titans.
Even the NYSE joined the online party, in 2006 abandoning the open-cry system that had been its iconic symbol even before Joaquin Miller was amazed by the pit’s “growling, howling, roaring, and bellowing” in 1880.
“What we did,” said Gordon Macklin, Nasdaq’s first CEO, “was to create a trading floor that was 3,000 miles long and 2,000 miles wide.”
An early sign that the robots had taken over came on May 6, 2010, with the five-minute selloff dubbed the “flash crash.” There was no apparent trigger for the Dow’s midday plunge of almost 1,000 points, about 9%. Suspicion centered on high-frequency-trading firms, whose computer-driven algorithms accounted for two-thirds of market volume, according to The Wall Street Journal.
“It was mayhem,” one trader told the Journal. Precisely what created this mayhem, and how, remains a point of debate, even as flash crashes strike other markets and asset classes.
The AI revolution promises to turn even more of the work of investing over to the machines. Research, due diligence, those pitchbooks now being prepared by human analysts—entire trading strategies may soon be under AI control.
As for Miller, his week on Wall Street stretched to six months, his initial $1,000 investment ballooning to $21,000 (an estimated $700,000 in 2026 dollars) before going bust. “I lost but little, having but little to lose,” he wrote. “And I learned so much, having so much to learn.”
Similarly, AI has a lot to learn—and a lot to lose.