Tech Talks

Modern AI, particularly large-scale language models (LLMs) and specialized reasoning architectures, has evolved beyond its role as a simple data analysis tool. It has become a new and unusual tool for scientific research, capable not only of processing massive data sets but also of simulating complex reasoning chains and virtual experiments. These chains – in physics, sequences of mathematically related computations – can be simulated in a unified digital environment.

By Juliet Mirambo Global trade has always been complex, but in recent years, the pace of regulatory change has accelerated. Companies now face a maze of overlapping rules that vary by country, region, and industry. A single overlooked regulation can lead to multimillion-dollar fines, blocked shipments, reputational damage, or even loss of market access. For businesses operating across borders, keeping up has become a high-stakes challenge.

By Purusoth Mahendran Most engineering teams build systems that work today, but the best teams build systems that survive orders of magnitude growth. The difference becomes apparent when transaction volume shifts from millions to billions, rigid workflows give way to conversational interfaces, and batch processing evolves into real-time intelligence.

By Jan Gilg The Era of Business AI has moved well beyond the initial hype. Today, it’s already taking its place at the center of executive decision-making. According to SAP’s recent AI Has a Seat in the C-Suite survey, nearly two-thirds of executives have integrated generative AI into their decision-making processes or are actively expanding its role. For many of the largest organizations, AI is replacing or significantly bypassing traditional methods.

Disclaimer: This forecast is a synthesis of current research trends and expert opinions. It is not financial advice, but rather a view of the potential long-term technological and economic development of the AI ​​industry created by the neural modeling lab of AI SYNT (Canada). I would like to introduce you to a very interesting document – an analysis of the current situation in the AI ​​industry and an incredible forecast covering the period up to 2045.

By Tobias Adler Artificial Intelligence (AI) is now a key component of contemporary business and technology, influencing everything from product development and research to customer service and logistics. As adoption deepens, the demand for infrastructure capable of supporting increasingly complex workloads grows in parallel.

In this article, I will tell you why the technological singularity has already arrived despite the fact that most of us still do not understand that scientific and technical progress no longer belongs only to the human brain. Using a specific example, I will show how artificial intelligence creates new technical solutions and creatively overcomes the limitations of the human brain. Most of us look at AI as an analogue of a search engine with the function of analysis and dialogue.

By Prithvish Doshi For years, AI has been talked about in terms of narrow tasks and statistical predictions. But that’s changing fast. With the rise of large language models and increasingly capable autonomous systems, we’re entering a phase where AI is not just generating outputs but making decisions and taking actions. The shift toward agentic AI, systems that can plan, execute, and adapt, is no longer confined to academic papers or open-source demos.

By Mathis Joffre As AI agents grow in complexity and capability, organizations are racing to deploy them across real-world environments. But many hit an unexpected bottleneck: the limitations of today’s serverless cloud infrastructure.

By Tom Chavez and Adrien Le Gouvello Today, 70% of U.S. stock trades are executed by algorithms, not humans. In digital advertising, real-time bidding systems make billions of ad placement decisions daily, with autonomous software determining what to buy, when, and at what price—all within milliseconds. These aren’t isolated examples.