Evolution to Generative AI: Deep Blue to the Terminator
Artificial General Intelligence (AGI) goes beyond generative AI and refers to a system like Skynet from the James Cameron Terminator movies that can think, learn, respond and perform tasks cognitively like a human. Whether it takes 5 or 15 years to develop, we most likely will see this advancement in our lifetime as newer generations of generative Ai develop quickly.
I’ll Be Back
The Terminator himself was part of a series of machines created by Skynet, an artificial intelligence, for infiltration-based military surveillance system, sent back from the year 2029 to 1984 with no sense of human morality. It’s kind of amazing and scary at the same time to see the swift advancement in technology today that may be leading to hopefully more humane and productive typed AGI platforms.
During a recent Q&A with Arnold Schwarzenegger, he applauded Cameron’s 1984 film for predicting the rise of ai 40 years ago. The actor shared that the idea of the Terminator is now becoming a reality thanks to modern tech.
Deep Blue and Chess
In the annals of artificial intelligence history, one of the most notable milestones occurred 27 years ago in 1997 when IBM’s Deep Blue, a supercomputer, defeated the then reigning world chess champion Garry Kasparov. This event marked the first time a computer won a match against a world champion under standard chess tournament time controls, showcasing the capabilities of AI in specific, highly structured tasks.
Created to play chess by IBM, Deep Blue consisted of two 2-meter-tall towers, more than 500 processors, and 216 accelerator chips. It was not the first instance of a computer competing against a human in chess, but it was the first to defeat a world champion in a standard match. The six-game match took place in May 1997, and Deep Blue won 3.5-2.5. This victory was achieved through brute force computational power, leveraging its ability to evaluate 200 million positions per second.
Jon here. You may notice that I write a bit on tech and finance. This is not by coincidence. In the early ‘00’s I ran quarterly workplace financial education programs for IBM employees at their headquarters in Boca Raton, and am a member of the IBM SF Alumni Association. It was there in 1981 that the first IBM PC personal computer was built and introduced to the world. Walking down the halls you would see pictures outlining the PC’s evolution like a March of Progress.
While a lot led up to the dotcom bubble and crash with thousands of IPO’s failing like pets.com, Amazon, eBay, and Priceline were among the companies that managed to survive and adapt through reorganization, new leadership, and redefined business plans. Moving forward from the dotcom internet era to the new ai era only took a couple decades. It seems fast and slow at the same time. Just don’t blink.
The Evolution to Generative AI
The journey from Deep Blue’s rule-based chess prowess to the sophisticated, human-like generative capabilities of modern AI illustrates the evolution of artificial intelligence. Deep Blue’s success was a testament to the power of specialized, high-performance computing, but it was limited to a narrow domain. In contrast, generative AI models like GPT-4 are more versatile, adaptive, and capable of understanding and generating human-like content across various fields.
Generative AI represents a more profound emulation of human cognitive processes, learning from vast datasets and making nuanced connections that resemble human thought patterns. This shift from brute force computation to sophisticated learning algorithms underscores the ongoing evolution of AI, paving the way for even more advanced and intuitive technologies in the future. Nvidia’s CEO Jensen Huang was recently discussing on CNBC that evolutions in new tech innovation improvements are now cycling to newer generations in less than one year – that previously took nearly 3-5 years to develop.
A Broader Impact Across Industries
Investors and the financial media tend to focus on shorter term macroeconomic concerns such as inflation, labor markets, and the Fed. While these topics are important, history shows that the economy and markets grow over long periods of time due to technological innovation and gains in productivity.
It’s for this reason that recent developments in artificial intelligence have captured the attention of investors and economists. They have also led to disagreements about the short-term investment opportunities across technology stocks and, more importantly, about the long-term effects on the economy, productivity and the labor market.
The implications of generative AI extend far beyond specific products or services. Its integration into various sectors can lead to more efficient processes, reduced costs, and enhanced innovation. Unlike the dotcom era, where the focus was on creating new companies and consumer experiences, the generative AI revolution is about enhancing the capabilities of existing industries and enabling them to operate at new levels of efficiency and creativity.
Some major examples of the “new” ai advancements include healthcare where AI-driven tools can assist doctors in diagnosing diseases more accurately by analyzing medical images and patient data. They can also personalize treatment plans based on an individual’s genetic makeup and medical history, leading to better patient outcomes. Another example will be advancements in education where AI will provide personalized learning experiences for students, adapting to their individual needs and learning styles. This can help bridge educational gaps and ensure that all students have the opportunity to succeed.
How Innovation Affects the Stock Market
The key, however, is for these investments and their returns across industries to correspond to true innovation. As the accompanying chart shows (below), the past century has been one of unceasing technological advancement from the first assembly lines to putting a man on the moon, to the entire world being connected on a single information network. In fact, the modern era is driven by the idea that technology should improve every year, whether it’s our cell phones, computers, cars or thermostats. In turn, the services they enable should also improve the quality of our lives, including ordering food delivery, watching movies on demand, communicating with friends and family, better medical care, and making investment decisions.
History of Innovation
What makes the information age unlike previous periods in history? Unlike physical objects such as buildings, machines on factory floors, or hardcover books, one person’s use of a digital service doesn’t prevent another from using it. This is important because digital tools allow knowledge workers to be more productive in a scalable way, creating benefits across a variety of industries and scientific fields. Knowledge workers are also expensive and time-consuming to train, so even small gains in productivity can compound over time.
These are all reasons why many believe that AI is the pinnacle of the information age. Today’s optimism on AI stems from the idea that it could enable even more technological innovations and accelerate productivity further. So far, examples of AI-boosted productivity in the business world have mainly been in the form of generating content, automating tasks, and analyzing data. It’s hard to predict exactly what impact AI will eventually have, partly due to the pace of advancement, and partly because the impact of new technologies is simply hard to imagine. What’s clear from the chart above is that the stock market did not move up in a straight line with past innovations, and many of their effects took years to materialize.
AI stocks have outperformed but other sectors are benefiting too
As computer scientist Roy Amara said, investors tend to overestimate the impact of technology in the short-term and underestimate the effect in the long run. There are many examples of this throughout history, most recently with the dot-com boom and bust. In the late 1990s and early 2000s, many companies rushed to add .com and .net to their names, capitalizing on the demand for all things internet related. This also occurred during the “tronics boom” of the early 1960s when a wave of companies with that suffix in their names benefited from investor exuberance for the consumer and business adoption of electronics.
While these trends generated significant returns for many years, the bubbles eventually burst. In hindsight, investor capital was clearly not put to its best use despite the promises of outsized returns. The challenge is knowing if there is a bubble to begin with. Today, some are rebranding themselves as AI companies, updating their websites, and using .ai domain names – no different than in the 1990s and 1960s. The performance of large cap stocks has been driven by a handful of companies related to technology and AI, including the Magnificent 7. Companies like Nvidia, for instance, have become household names when only a couple years ago they were primarily known for video game graphics cards.
There are clear differences between today’s market and the dot-com bubble, the most important being that many large cap companies benefiting from these trends are highly profitable. There is also nothing wrong with taking advantage of a market trend or long-term investment theme. However, history shows that the problem occurs when trying to either time the market or making concentrated bets that are inappropriate for one’s portfolio. The accompanying chart shows that while technology-related sectors such as Communication Services and Information Technology have outperformed, other sectors have done well this year too. Having too narrow a focus on AI trends, at the expense of overall portfolio diversification, can be problematic for investors as they work toward their long-term goals.
Productivity is what drives long run economic growth
Having a short-term perspective is also problematic because the benefits of past technological innovations were much broader than a few stocks over a year or two. For instance, the benefit of the information technology revolution driven by the rise of personal computers and the growth of internet adoption was not, in hindsight, just concentrated in a few dot-com stocks in the 1990s. Instead, the benefits were broad across industries as all companies adopted new technologies that improved their productivity. In fact, the true impact of these technologies took decades to come to fruition and are still affecting markets today.
Why is productivity so important? In short, productivity allows us to do more with less. This can be difficult to measure and may not always show up in the macroeconomic data the way we expected. When the personal computer and the internet were developed, productivity was relatively stagnant. In the 1980s, the average year-over-year productivity gain was just 1.5%, lower than the previous three decades. Economist Robert Solow called this the “productivity paradox,” saying in 1987, “you can see the computer age everywhere but in the productivity statistics.”
Productivity growth finally began accelerating in the early 1990s and remained strong into the early 2000s. This occurred alongside fundamental changes in how businesses delivered products and services. It is difficult to predict how quickly this might occur with AI and how it will transform industries and societies – which is why such predictions are controversial among AI scientists and economists. In a recent paper, MIT economist Daron Acemoglu argues that AI will have its biggest impact if it results in new, higher-value tasks for workers, but that these benefits are not forgone conclusions nor are they likely to be on the scale that some predict.
The bottom line? While recent advancements are impressive, it could take time for generative AI applications to enhance the productivity of workers and flow into increasing corporate profits over time across industries.
On the one hand, it’s possible as we discussed at the top of today’s newsletter that we are on the cusp of artificial general intelligence (AGI) – technology that could replace human workers altogether. On the other hand, it’s possible that generative AI will require the right applications and adoption to be valuable. In the meantime, the market will try to gauge what value to place on these possibilities in the stock market. It’s important for long-term investors to maintain a broader perspective and not get caught up in the day-to-day hype.
For more information on our firm or to request a complementary investment and retirement check-up with Jon W. Ulin, CFP®, please call us at (561) 210-7887 or email jon.ulin@ulinwealth.com.
Note: Diversification does not ensure a profit or guarantee against loss. You cannot invest directly in an index.
Information provided on tax and estate planning is not intended to be a substitute for specific individualized tax or legal advice. We suggest that you discuss your specific situation with a qualified tax or legal advisor.
You cannot invest directly in an index. Past performance is no guarantee of future returns. Diversification does not ensure a profit or guarantee against loss. All examples and charts shown are hypothetical used for illustrative purposes only and do not represent any actual investment. The information given herein is taken from sources that are believed to be reliable, but it is not guaranteed by us as to accuracy or completeness. This is for informational purposes only and in no event should be construed as an offer to sell or solicitation of an offer to buy any securities or products. Please consult your tax and/or legal advisor before implementing any tax and/or legal related strategies mentioned in this publication as NewEdge Advisors, LLC does not provide tax and/or legal advice. Opinions expressed are subject to change without notice and do not take into account the particular investment objectives, financial situation, or needs of individual investors.
