Opinion

Abu Dhabi has announced Aion Sentia, the world’s first city designed to operate as a unified artificial intelligence. This is a core part of the UAE’s national strategy to build an AI-managed infrastructure, backed by a $2.5 billion investment from a consortium of My Aion Inc. and Bold Technologies, with a launch target of 2027. At its heart is MAIA, a Multi-Agent Artificial Intelligence platform that acts as the city’s central nervous system. It’s designed to integrate transport, energy, healthcare, and public services into a single, cohesive whole. Over 200 AI modules will continuously learn from resident behavior, adapting and making decisions in real-time. The objective is to create a unified cognitive system, not just a collection of disconnected “smart solutions.” ...

This isn’t just another milestone in DNA editing technology. It’s a landmark moment that signals the beginning of a new era: personalized, genetically-targeted medicine. These are treatments created for a specific person, for their unique mutation, for their precise diagnosis. This is the future of medicine, and it’s happening today. I have been enthusiastically following the development of Cas9 technology since 2012—the very protein that kicked off the era of precision DNA editing. The world later came to know this field as CRISPR, or more simply, “genetic scissors.” ...

There are two core approaches to management. Both can work, but one suppresses initiative while the other cultivates it. The first is the “Task + Control” model. A manager assigns a task, monitors its execution, and only requires the employee to do exactly what was asked. Initiative is not part of the equation. The second, more common in Western tech circles, is the “Area of Responsibility + Boundaries” model. Here, you don’t receive a task; you receive a domain. You are given the objective, the resources, and the strategic guardrails. What happens next is your growth zone. You apply your expertise, think critically, and take initiative. Sometimes you make mistakes. But often, you deliver a better outcome than anyone could have prescribed from the top. ...

I’m often asked which AI training project cost millions of dollars and two years of my life. People wonder: why is it so expensive? My usual answer is that it’s not particularly expensive—especially considering we don’t own our own hardware yet. Training AI has always been about massive data centers; that’s just the reality of the field. When you’re not immersed in it, the sheer scale can be hard to visualize. ...

This series of posts will be my way of documenting the journey of creating one of our team’s most ambitious products: the intelligent assistant, A.V.E.L.I.N. To give you some context, my development team and I are currently beta-testing the project within our Mozgii Ecosystem AI platform. Our primary focus is on A.V.E.L.I.N.—an intelligent personal assistant in Telegram built to handle both basic and complex tasks involving AI-powered search, processing, and analysis of information. ...

From my experience working with neural networks, it’s become obvious how two people can interact with the same model and get radically different outcomes. This isn’t a minor variation—it signals a fundamental departure from the search engine paradigm. We have entered a new era of interacting with artificial intelligence. AI doesn’t just aggregate data; it selects and synthesizes relevant information in response to specific requests. This changes the very nature of how we engage with information. Where the key skill was once finding data in search engines, it is now the ability to correctly formulate requests to an AI. ...

I’m often asked why international AI models, like those from OpenAI, consistently outperform Russian counterparts such as GigaChat. To understand the gap, we need to look beyond the code and analyze the foundational, structural challenges. Here are the key factors limiting Russia’s position in the global AI race. 1. The Compute Bottleneck Effective AI development at scale depends on raw computational power. Since 2022, access to essential high-performance NVIDIA chips (like the A100 and H100) has been severed. Training a model on the scale of GPT-4 requires a cluster of over 10,000 GPUs—a resource capacity that simply doesn’t exist in Russia. For context, Sber’s most powerful supercomputer, Christofari Neo, operates at around 12 petaflops, making it 50 to 100 times less powerful than the world’s leading AI research centers. ...

OpenAI has released Codex CLI, an open-source AI agent for developers. This marks a quiet but significant victory for the open-source community. The tool allows developers to use natural language directly in the terminal—the agent interprets the request, then writes, executes, and tests the code. Most importantly, this entire process runs locally, without sending data to the cloud. With this release, the industry moves one step closer to a system that can independently understand, build, and deploy solutions. It underscores a critical point: the future isn’t just about choosing the right model, but about engineering the right architecture that connects thought → action. ...

Yesterday, OpenAI opened access to the GPT-4.1 API. It’s a refined version of their flagship model—faster and architecturally closer to the concept of ‘agents.’ In parallel, the company officially announced it is winding down GPT-4.5, its most resource-intensive model, due to its excessive complexity and support challenges. With GPT-4.5, it seems they hit an architectural dead end. We are at a point where models appear and disappear rapidly. They are becoming what they should be: tools, not landmark events. We have a growing catalog of specialized AIs: some calculate, others write code, plan tasks, or generate video. But the average user should not be expected to know and choose between every AI in existence. That paradigm defies the logic of good user experience. ...

Your Thought Process, Packaged Deep Research isn’t just another AI feature; it’s a fundamental shift toward an agent-based architecture. In this model, the LLM stops being a simple chatbot and becomes a co-author—an agent that independently searches, filters, validates, and structures information. What does this change? If you’re designing a business, a startup, or a product, you don’t have time to personally read 200 sources. Now, an AI agent does it for you. This frees you up to do the high-value work: to think, not just to search. ...