Until recently, running large language models was a process with a clear ceiling – the amount of available memory. If RAM was insufficient, the system would either refuse to start or run so slowly that it lost any practical meaning. This formed a persistent belief that the development of artificial intelligence depends solely on purchasing new batches of powerful GPUs. However, the engineering focus is now shifting toward algorithm efficiency rather than scaling up hardware.
Once, the physical security of a data center seemed straightforward and even linear: a solid door, a strict guard at the post, and a few cameras were enough. Back then, that was completely sufficient, because the facilities themselves were smaller, and their role was not as critical. Today, however, a data center is the “heart” of business and banking systems, so the approach to protection has changed. It is no longer enough to simply keep outsiders behind closed doors. It becomes important to see every corner of the site in real time, react instantly to the slightest deviations in equipment operation, and eliminate risks before they turn into a real incident.
Today discussions about artificial intelligence are gradually moving out of the “will it replace or will it not” debate into the sphere of practical task management. In practice we are not seeing mass disappearance of professions, but a redistribution of roles. AI becomes another tool in the stack, to which the technical part is delegated, while architectural oversight and responsibility for the final release remain with a human.
Just a few years ago, the digital lives of most people consisted of dozens of separate applications. Asana or Monday were used for task planning, GitLab for working with code, Wix for building websites, Duolingo for online learning, and specialized support platforms for working with clients. Each program performed a narrow function, and this was considered a normal model of computer use. Today, this logic is gradually changing. Artificial intelligence is taking over more and more tasks that previously required separate programs, and it does so within a single universal environment.
Until recently, artificial intelligence was perceived mainly as a software technology. It analyzed texts and images, helped with information search, or automated routine digital tasks. Today, however, AI is rapidly moving beyond screens and beginning to operate in the physical world. This shift became clearly visible after recent statements at technology exhibitions, where it was demonstrated that artificial intelligence is transitioning from a supporting tool to an active participant in real-world processes.
Just a few years ago, automation was primarily associated with factories, assembly lines, and physical labor. It was believed that intellectual professions related to analysis, writing, law, or management would remain an area of human advantage. However, the rapid development of artificial intelligence has changed this logic. Today, it is precisely the segment of the labor market that was long considered the most protected that has come under pressure — work based on knowledge, experience, and information processing.
For many years, RAM remained a relatively affordable and predictable component of computer technology. Performance growth was accompanied by declining costs, and increasing the amount of RAM was considered a simple way to scale almost any project. However, today this model is rapidly collapsing. The world is entering an era in which RAM becomes a scarce resource, and its price and availability are determined by global infrastructure projects.
The world of digital content is changing faster than ever, and image generation has become one of the key technologies driving this transformation. Just a few years ago, AI-generated images looked like an interesting but quality-limited experimental feature. Today, they are evolving into a full-fledged professional tool. Another major step in this direction is the launch of ChatGPT Images — a new image generator from OpenAI that is already being called the company’s most powerful solution in the visual domain.
Artificial intelligence has gradually ceased to be an experimental technology and has become part of everyday business practice. Today, AI is used for big data analytics, process automation, forecasting, and language and image processing. All of this creates new requirements for server infrastructure. If previously the main focus was on stability and data storage, now performance, scalability, and the ability to handle constantly growing workloads play a key role. It is in this context that IBM introduced Power11 — a new generation of servers designed to operate in the conditions of the AI reality.
In the coming years, the global computer hardware industry may face the most serious shortage of the past decade. The market for dynamic RAM (DRAM) and solid-state drives (SSD) is experiencing a sharp rise in prices and a shortage of components, affecting both electronics manufacturers and ordinary users. The reason behind this is the rapid shift of leading companies toward producing chips for artificial intelligence, which today consumes most of the world’s manufacturing capacity. To understand what this may lead to, it is worth examining the factors driving the new memory crisis.