“Oh No, Not Again…” — Why Germany Still Has a Chance in AI

Historical commentary. First published in German on 24 September 2025. This English translation incorporates the corrections made to the German text on 4 October 2026 and retains the original time perspective. English translation published on 16 October 2026.

Artificial intelligence is advancing rapidly, and it can often seem as though only the major players in the United States and China have a say in where it goes next. Yet this is precisely where a particular opportunity opens up for Germany and Europe. Three observations show where our potential for efficient AI lies.

Why language models hallucinate

In its research on “Why Language Models Hallucinate”, OpenAI examines why language models produce plausible but incorrect answers. The paper on arXiv shows, among other things, how training and evaluation can reward guessing when a model is uncertain. It does not prove that hallucinations are fundamentally impossible to resolve in every AI system. A 2024 study in Nature investigates statistical methods for detecting a particular subset of these errors.

In practice, this means that even the best models available at the time will continue to generate incorrect facts that can be difficult to spot. Bigger models, faster processing and more data alone will not solve this structural problem. This is precisely where research into fundamentally new approaches is needed.

What are hallucinations in AI models?

“Hallucinations” are responses from AI systems that sound linguistically plausible but are factually incorrect or entirely invented. One example would be an AI answering a question about a historical figure with a quotation that person never said. This is a structural problem of today's language models rather than simply a software bug. They work by predicting text on the basis of probabilities, rather than possessing a genuine understanding of truth. However, this way of working does not establish a general proof that the problem is unsolvable. For example, a system can refrain from giving a specific answer when it is uncertain.

The business model of the major technology companies

Companies such as NVIDIA, Microsoft and Google are currently pursuing ever larger models. The NVIDIA CES 2025 keynote and the GTC 2025 event clearly show how their commercial success rests on selling computing power. More models mean more GPUs and more revenue.

As analyses of the CES 2025 announcements show, the focus is on ever more powerful data centres and larger AI infrastructure. Efficient, lean AI is not their current priority, and I do not expect that emphasis to change fundamentally over the next few years. For the major providers, encouraging resource consumption is economically attractive; reducing it is less so.

Europe's opportunity in fundamental research

This is where Europe can make its mark. The future of AI will not be decided solely by the race to build ever larger models. New models, technologies and approaches are needed. German and European researchers can excel at precisely this intersection.

The EU adopted €7.3 billion in funding for research and innovation across several fields under the Horizon Europe work programme for 2025. The DIGITAL work programme for 2025–2027 includes a further €1.3 billion for deploying strategic digital technologies, including AI, cloud and data, cybersecurity and digital skills.

Fundamental research into efficiency, new algorithms and approaches that use fewer resources presents a major opportunity. Far from having lost our chance, we have an opportunity to gain a foothold and deliberately pursue new research directions. The prize is still up for grabs, and Europe has the means to compete for it.

A different approach in practice: Apple's on-device AI

With its Apple Intelligence features, Apple is pursuing a strategy that differs fundamentally from its competitors'. It brings many AI features directly to users' devices through on-device AI, complemented by larger server models for Private Cloud Compute.

As technical analyses show, this substantially reduces the resources required in data centres, strengthens privacy and opens up new possibilities for using AI in everyday life. The approach illustrates that innovation need not always mean “bigger”. Sometimes progress lies in efficiency.

At the same time, the market for small language models, or SLMs, is developing rapidly, alongside edge AI hardware setting new benchmarks in 2025.

Conclusion

The AI revolution is well under way, and Europe has every opportunity to keep pace and lead in crucial areas. While the major corporations pursue scale, Europe can take a leading role in fundamental research, efficiency and innovation. The time to act is now. We have not lost our chance to win.

Related reading added on 16 October 2026: For a later perspective on Europe's role in AI and the responsibility of technology providers, see my analysis of Zuckerberg's vision and European AI governance.

Related reading added on 18 October 2026: AI After 60 explains how critical checking can go together with an encouraging start: through everyday experiments, the dog-training analogy and the question “Does that make sense?”.