Science and Business

In the world of science, sometimes the most profound insights come from the simplest of experiments. In the early 1980s, at the dawn of the personal computing era, a political scientist named Robert Axelrod set up a digital arena to pit computer programs—each with its own "personality"—against each other in a classic game of strategy. The results were not just surprising; they were groundbreaking, offering a powerful new lens through which to view the evolution of cooperation itself.

In a previous analysis, we identified technological revolutions as the primary engine of the long economic waves known as Kondratiev cycles. The steam engine, railways, electricity, and the microchip were all fundamental innovations that reshaped the global economy in recurring 50-to-60-year cycles. However, this technology-centric view tells only one part of the story—albeit a spectacular one. Behind the scenes, other equally powerful forces are at play: the flow of financial capital, the shifting tides of social mood, and the realignment of global power.

As we navigate the daily flood of economic news—quarterly reports, market fluctuations, and short-term business cycles—it’s easy to get lost in the noise. But what if there’s a deeper, slower, and more profound rhythm shaping our world? A pattern that connects the age of the steam engine to the rise of railways, the era of electricity to the automobile, and the dawn of the computer to the rise of artificial intelligence?

There exists a unique kind of logical trap where the solution to a problem is blocked by the very conditions required to solve it. This inescapable, self-referential dilemma is known as a "Catch-22," a term that escaped the pages of Joseph Heller's 1961 novel to become one of the most apt metaphors for modern life. It has since grown far beyond its literary roots to become a universal symbol for the absurdities of bureaucracy, power, and everyday existence.

The introduction of Google's AI Overviews marks a turning point in the evolution of the internet, catalyzing a paradigm shift from a referral-based web to an answer-centric ecosystem. This transformation, driven by generative artificial intelligence, is fundamentally changing the long-standing symbiotic relationship between search engines, content creators, and users.

How many friends do you really have? The number of your Facebook connections might run into the hundreds or even thousands, but with how many people do you maintain a truly deep and meaningful relationship?

Why is it so difficult to predict the weather more than a week in advance? Why can a tiny change in a system lead to a dramatically different outcome? These are the kinds of questions that led to the development of one of modern science's most fascinating fields: chaos theory.

Occam's Razor, the principle often summarized as "the simplest explanation is usually the best," is one of the most pervasive and practical heuristics in human thought.

The term "Singularity" has transcended niche scientific discourse to become a recurring motif in popular culture, featuring prominently in films, news articles, and public debate. Often depicted with dramatic flair, it evokes images of runaway artificial intelligence and fundamentally altered human existence. While sometimes sensationalized, the underlying concept warrants serious consideration, particularly as advancements in Artificial Intelligence (AI) accelerate.

Game theory, the mathematical modeling of strategic decision-making, employs numerous concepts to help understand the dynamics of interaction. One of the most important and frequently cited among these is the zero-sum game. This concept describes situations where one participant's gain necessarily results in another participant's loss, and the total "winnings" remain constant, effectively summing to zero. Zero-sum games serve as fundamental models for competition and conflict, holding relevance across various domains, from sports and economics to politics.

Have you ever wondered why modern technology, supposedly designed to make our lives easier and save us time, doesn't actually result in more free time? Why do we work just as much, or perhaps even more, than our grandparents, despite being surrounded by washing machines, dishwashers, computers, and smartphones? The answer lies in a phenomenon recognized back in the Industrial Revolution, known as the Jevons Paradox.

Reverse Polish Notation (RPN) is an efficient method for evaluating mathematical expressions, characterized by placing operators after their operands. This approach allows for the omission of parentheses, simplifying and clarifying the calculation process. Although it might seem different at first, using RPN significantly speeds up the execution of operations, especially in computer systems and programmable calculators.

Many people still associate graphics cards primarily with gaming, yet GPUs are capable of much more. Due to their architecture, they are excellently suited for parallel computations, which is essential for training and running deep learning models. Consider this: a modern LLM has billions of parameters, and all these parameters need to be managed simultaneously. This type of parallel processing is the true strength of GPUs, whereas traditional CPUs (central processing units) lag behind in this regard.

Throughout the evolution of storage devices, numerous connection technologies have emerged, bringing revolutionary changes in both speed and efficiency. SATA, SAS, and M.2 connections are among the most common solutions today, but what are they used for, and how do they differ?

Efficient operation of Large Language Models (LLMs) heavily relies on the appropriate storage and processing of their weights. The chosen numerical format directly impacts the model's memory requirements, computational speed, and accuracy. Over the years, FP32 has gradually been supplemented or replaced by FP16 and BF16 for training, while INT8 and even lower-bit quantized formats are increasingly common for optimizing inference.

The rapid development of Large Language Models (LLMs) poses new challenges in the field of computing. A crucial question for me is how GPUs perform when running these models. In this post, I aim to examine the performance of various GPUs through the concepts of TFLOPS (trillion floating-point operations per second) and TOPS (trillion operations per second). I will present the capabilities of individual models using a clear table, supplemented with brief explanations.

Graphics card prices have been sky-high for years, and this is due to complex, interconnected reasons. Often, a high-end graphics card (GPU) alone can cost more than all the other components of a computer combined, highlighting just how significant their price has become.

Data transfer speed plays a crucial role in the performance of modern computers. USB and PCIe interfaces have evolved over decades to provide increasingly faster and more efficient communication between devices. In this post, I'll review the speeds of these connections and their evolution across generations.

The performance of modern computers is significantly influenced by the speed and type of memory used. DDR, GDDR, and HBM memories are optimized for different purposes, whether it's main system memory for CPUs, memory for graphics cards, or tasks requiring high bandwidth computations. In this post, I compare the speeds and key parameters of these memory types.

There are many misunderstandings surrounding the speeds of various computer data transfer and storage standards (e.g., USB, SATA, DDR, PCIe, Ethernet, etc.). That's why I've created several summary descriptions and tables covering the main standards and their speeds.