Ted Lamade is the Managing Director at The Carnegie institute for Science and has written some insightful guest pieces for the Collab Fund. In this piece, he addresses a topic that we have been featuring quite extensively at the 3L&3S of late – Artificial Intelligence. He first attacks the doomsayers who are calling AI will destroy humanity:

“…most people today, investors in particular, seem to be treating each and every “acorn” (i.e. negative headlines) as a sure fire indication that the economy and/or markets are bound to crash.

Just think about the last decade alone. From Covid-19 to disinformation, crypto and the FTX fraud, Iran, China, Russia, climate change, a tech bubble 2.0, supply chain shortages, globalization, Silicon Valley Bank’s collapse, office vacancies, and higher interest rates (just to name a few) have all been deemed perilous threats to financial and/or geopolitical stability. Yet we are still here with unemployment close to all-time lows and the stock market near record highs.”

Instead he says: “..AI is likely going to impact sectors of the economy and markets very differently. Understanding how is the first step towards not fearing its arrival.”

He then shares his take on the possible ways AI is likely to impact various sectors ranging from education, healthcare, consumer, industrial to geo-politics. His take on finance given the relevance to us and for the hilarious quote from Matt Levine of Bloomberg:

Lamade says: “…I can’t imagine a sector that will experience more booms and busts than finance as a result of AI.” Before going on to quote Matt Levine:

“The widespread use of relatively early-stage AI will introduce new ways of making mistakes into finance. Right now there are some classic ways of making mistakes in finance, and they periodically lead to consequences ranging from funny embarrassment through multimillion-dollar trading loss up to systemic financial crises. Many of the most classic mistakes have the broad shape of “overly confident generalizing from limited historical data,” though some are, like, hitting the wrong button. But there are only so many ways to go wrong, and they are all sort of intuitive. But now there are new ways! Weird ways! Oh sure an AI can probably make overly confident generalizations from limited historical data, but perhaps there is room for novelty. Now some banker is going to type into a chat bot “our client wants to hedge the risk of the Turkish election,” and the chatbot will be like “she should sell some Dogecoin call options and use the proceeds to buy a lot of nickel futures,” and the banker will be like “weird okay whatever.”

And that trade will go wrong in surprising ways, the client will sue, the client and the banker and the chatbot will all come to court, the judge will ask the chat bot “well why would this trade hedge anything,” and the chatbot will shrug its little imaginary shoulders and be like “bro why are you asking me I’m a chat bot.” Or it will say “actually the Dogecoin/nickel spread was ex ante an excellent proxy for Turkish political risk because” and then emit a series of ones and zeros and emojis and high-pitched noises that you and I and the judge can’t understand but that make perfect sense to the chat bot. New ways to be wrong! It will make life more exciting for financial columnists, for a bit, before we are all replaced by the chat bots.”

He ends with this perspective on why AI and human combination will only mean more progress:
“In his best selling book, “Range”, author David Epstein profiled a chess match between chess-master Gary Casparov and IBM’s Supercomputer Deep Blue in 1997. After losing to Deep Blue, Casparov responded reticently that,
“Anything we can do, machines will do it better. If we can codify it and pass it to computers, they will do it better”.

However, after studying the match more deeply, Casparov became convinced that something else was at play. In short, he turned to “Moravec’s Paradox”, which makes the case that,
“Machines and humans have opposite strengths and weaknesses. Therefore, the optimal scenario might be one in which the two work in tandem.”

In chess, it boils down to tactics vs. strategy. While tactics are short combinations of moves used to get an immediate advantage, strategy refers to the bigger picture planning needed to win the game. The key is that while machines are tactically flawless, they are much less capable of strategizing because strategy involves creativity.

Casparov determined through a series of chess scenarios that the optimal chess player was not Big Blue or an even more powerful machine. Instead, it came in the form of a human “coaching” multiple computers. The coach would first instruct a computer on what to examine. Then, the coach would synthesize this information in order to form an overall strategy and execute on it. These combo human/computer teams proved to be far superior, earning the nickname “centaurs”.

By taking care of the tactics, computers enabled the humans to do what they do best — strategize.”

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