Computing encompasses a set of disciplines related to the automated processing of information, from physical hardware to software layers, including networks and data security. Since 2024, several technical mutations have reshaped this scope: the integration of artificial intelligence in development environments, new hardware architectures oriented towards neural computing, and a European regulatory framework that imposes direct constraints on project teams.
EU AI Act: What the regulatory framework changes for IT projects
The EU AI Act was definitively adopted in 2024, with a phased implementation. This regulation classifies artificial intelligence systems by risk levels and imposes specific obligations on both publishers and integrators.
You may also like : Discover the must-have beauty trends of this year and our tips for embracing them
For IT teams, the consequences are tangible. Systems classified as high risk (automated recruitment, banking scoring, AI-assisted medical devices) must comply with documentation obligations, model transparency, and training data management.
Even internal automation projects are affected as soon as they involve decision-making that impacts individuals. Data governance becomes a technical prerequisite, not just a legal one. An enterprise chatbot project, for example, must plan from its design stage for a register of the data used for training and a mechanism to explain the provided responses.
See also : The keys to understanding business evolution and anticipating future trends
Exploring The Web Brains’ computing universe allows you to follow these regulatory developments and their repercussions on digital professions.
AI Co-programming: GitHub Copilot, CodeWhisperer, and the evolution of code in the enterprise
Since 2023, generative AI tools integrated into IDEs have changed the way code is produced. GitHub Copilot, Amazon CodeWhisperer, Google Duet AI, and Microsoft Copilot do not just complete lines: they suggest entire blocks, review existing code, and propose corrections.
According to reports published by GitHub in 2023 and 2024, a significant portion of code in enterprises and open source is now suggested or reviewed by AI. Developers spend more time validating, correcting, and guiding than writing each instruction manually.
What this changes in daily life for a developer
The gain is not just in writing speed. Co-programming alters the nature of the skills sought:
- The ability to formulate a precise technical prompt (describing expected behavior, edge cases, performance constraints) becomes as useful as the syntactic mastery of a language.
- Critical review of AI-generated code takes center stage, as suggestions can introduce security vulnerabilities or unwanted dependencies.
- Unit testing and continuous integration become the safety net that absorbs the increased production speed without degrading quality.
This shift affects all languages and all profiles, from junior developers to technical leads. Announcements made by Microsoft at Build 2024 confirm that this integration will extend to office suites and project management tools.
AI-oriented PCs: new hardware architectures and impact on the workplace
Since late 2023, Microsoft, Qualcomm, Intel, and AMD have been pushing a new generation of PCs equipped with NPUs (Neural Processing Units). These chips dedicated to neural computing allow for local execution of artificial intelligence tasks without relying on the cloud.
The difference from a classic processor lies in the architecture: an NPU is optimized for massively parallel matrix operations, typical of neural networks. A CPU processes these calculations but consumes much more energy and time for an equivalent result.
Concrete uses in a professional setting
Local AI processing opens possibilities that did not exist on a laptop two years ago. Real-time voice transcription, simultaneous translation during video conferences, intelligent background blurring, and on-site medical image analysis now work without an internet connection.
For companies subject to confidentiality constraints, local AI processing eliminates the transit of data to remote servers. Law firms, healthcare facilities, and industrial design offices find a direct advantage in terms of GDPR compliance.
Cloud computing and edge computing: where to draw the boundary for data processing
The cloud remains the foundation of the majority of professional IT infrastructures. Major providers (AWS, Azure, Google Cloud) continue to expand their catalogs of managed services, particularly around artificial intelligence and data analysis.
Edge computing does not replace the cloud. It complements it by bringing data processing closer to its physical source. A factory equipped with IoT sensors, a network of stores with visual stock recognition, an autonomous vehicle: all these cases require very low latency that the cloud alone cannot guarantee.
Criteria for choosing between cloud and edge
- The acceptable latency: below a few milliseconds, only local or edge processing meets the need.
- The volume of raw data: transferring terabytes of video to the cloud is expensive in bandwidth. Filtering and compressing at the edge reduces costs.
- Regulatory constraints: certain sectors require that data does not leave a defined geographical perimeter, which leads to a hybrid architecture.
The current trend is not to replace one model with another, but to create a distributed architecture where each layer handles what it does best. The workstation with NPU, the edge server at the periphery, and the centralized cloud form three complementary levels.
Computing in 2025 is characterized by this layering of technical layers, each subject to distinct regulatory, hardware, and economic constraints. The choice of architecture is no longer based solely on performance criteria but also on compliance, data sovereignty, and the ability to integrate AI where it adds measurable value.