I want to write a review of Michio Kaku’s book, The Future of Humanity. The snag is, I read it over a year ago, and the details are somewhat hazy. I might recall some concepts, but there’s likely a chunk that slipped from my memory.

The other day, I tried resorting to YouTube for another person’s take on the book or even a summary to jog my memory. The initial videos I stumbled upon weren’t quite what I needed; they featured talks by Professor Michio Kaku, which were interesting but not a direct review. In one of these talks, someone asked him about his knack for predicting the future. Kaku often delves into his beliefs about future developments in his books. The question probed how he arrived at these predictions. An example was brought up—decades ago, people envisioned flying cars by now. Kaku’s response was insightful. He pointed out that such predictions were typically made by artists or individuals without a professional background in data, statistics, equations, or algorithms. He emphasized the distinction between predictions by laypeople and those by professionals.

Moore’s Law

Shifting to AI and technology’s future, Kaku discussed his approach, rooted in Moore’s Law. This law posits that computing technology doubles every year or so. By following this trajectory, predictions about future technology trends become somewhat reliable. However, Kaku also acknowledged the slowdown of Moore’s Law and the eventual ceiling it will hit. The looming question is, what happens post that limit? Do we hit a standstill in innovation?

No. Currently, those at the forefront of technology are exploring the next phase, and it’s intriguing to consider the potential shift. Kaku hinted that Silicon Valley might transform into an old Rust Belt. The basis for the present technology lies in silicon, but to progress, alternative chemicals or elements need to be explored. While this evolution may happen elsewhere, examining the data, especially following Moore’s Law, allows for some plausible predictions. It’s a speculative realm, but reasonable assumptions can be made based on the available information.

Moneyball

This train of thought also brought to mind the Brilliant Idiots podcast episode featuring James Altucher. He shared how he used statistics to predict stock market trends years ago, eventually making a fortune by selling the software he developed. Interestingly, he claimed that the same principle he applied to the stock market became the foundation for the book Moneyball. Although I haven’t delved into Moneyball yet, the premise revolves around the baseball association using hard data to make more informed decisions. This marked a departure from the previous system, where decisions were often based on intuition and perhaps some selective information. By embracing empirical data, the baseball world underwent a transformation, leading to more accurate predictions.

Alger even asserted that the Philadelphia Eagles employed a similar method, leveraging data from players on the field to analyze patterns and optimize their performance, ultimately culminating in their Super Bowl win earlier this year. This shift from intuition-based decision-making to data-driven strategies has evidently made a significant impact, not only in sports but also in various fields, reflecting a broader trend in leveraging data for improved outcomes.

Many decisions based on data may seem counterintuitive to traditional coaching or play-calling methods. The success of the Philadelphia Eagles, particularly under the guidance of the individual responsible for these data-driven strategies, underscores the effectiveness of such an approach. However, this shift toward data-driven decision-making challenges the prevalent romantic notion of relying on gut intuition for making decisions. Despite the demonstrated success in fields like sports, society often clings to the idea of intuitive decision-making, considering it more human and authentic.

Intuition vs Data-Driven Decisions

Reflecting on this, I found myself naturally averse to approaching certain thoughts empirically. There’s a tendency to resist using logic, cold facts, and data when dealing with issues that feel personal or emotional. The inclination is to lean towards intuitive decision-making, which seems more familiar and “human.” The internal struggle between relying on feelings and embracing empirical thinking is a common societal challenge, prompting us to question the balance between intuition and data-driven approaches.

Tying it back to the book Everybody Lies. The book emphasizes the importance of relying on data and big facts derived from internet searches to gain a more accurate understanding of trends and inform decision-making. This aligns with the broader idea of using empirical evidence to guide our choices. Also supporting this general idea is the book Thinking in Bets by Annie Duke, which discusses making decisions based on probabilities like a good poker player.

In contemplating your own decisions and potential challenges, it’s tempting to believe in personal exceptionalism, thinking that you can overcome historical difficulties through sheer determination and hard work. However, considering the data and statistics, as emphasized by Charles Duhigg‘s insights, points to a more pragmatic approach. Treating life like a poker player, assessing probabilities, and making decisions based on likely outcomes aligns with a more rational and data-driven mindset.

It’s a reminder to yourself and others to follow the data, consider probabilities, and embrace statistical insights when making decisions. While uncertainties exist, relying on empirical evidence and a realistic understanding of probabilities can lead to more informed and successful outcomes in the long run.

Original draft written in July 2019.