Germany has reached a notable point in its AI strategy. After years dominated by research, model development and policy frameworks, the emphasis is increasingly shifting towards practical deployment. Artificial intelligence is now being tested under real-world conditions across local government, industry, robotics, health research and education. At the same time, regulatory sandboxes, sovereign cloud infrastructure and major European programmes for industrial AI are beginning to take shape. The decisive shift is therefore away from broad AI experimentation and towards concrete, measurable applications that can be integrated into existing processes on a permanent basis.

Just a few months ago, the direction of travel seemed obvious: more powerful models, larger data centres, faster releases and increasingly autonomous AI agents. Now the tone across the industry is beginning to change. Senior figures at Anthropic and OpenAI are openly discussing whether the development of the most capable AI systems should proceed more slowly. Other influential voices across the sector are beginning to support at least the basic principle.

When people talk about the race for artificial intelligence, they usually think of new models from OpenAI, Google, Anthropic or DeepSeek. In reality, however, the next phase of AI development is increasingly being decided elsewhere: in computing infrastructure. Germany is therefore investing billions of euros in new data centres. As of today, more large-scale AI data centre projects are being planned or built than ever before. The objective is clear: expand domestic computing capacity, strengthen digital sovereignty and reduce dependence on the major US cloud providers.

Most people begin working with artificial intelligence by writing a prompt. But as AI adoption becomes more sophisticated, one thing becomes increasingly clear: a single prompt is rarely enough. Modern AI workflows are evolving along a clear maturity ladder – from prompts to skills, workflows, agents and, ultimately, projects. These five levels do not represent different technologies, but different ways of organising work with AI. Understanding them allows organisations not only to use AI more effectively, but also to scale it and integrate it into existing business processes.

From today, 2 August 2026, another key part of the European Union's AI Act comes into force. The transparency obligations set out in Article 50 introduce the first legally binding rules requiring certain AI-generated content and AI systems to be clearly identified. The aim is to ensure that people know when they are interacting with artificial intelligence or viewing content that has been created or substantially manipulated using AI.

Artificial intelligence is no longer a futuristic concept on university campuses. For the overwhelming majority of students, it has become an integral part of everyday academic life—whether for research, learning, writing or programming. The latest CHE DataCHECK, Artificial Intelligence in Higher Education, reveals just how deeply AI has become embedded in students' daily routines. At the same time, the report exposes a widening gap between how students actually use AI and the support universities provide.