When machines learn their own nonsense
Artificial intelligence is increasingly shaping everyday life and the economy - but at the same time, doubts are growing about quality, ethics and how it works. When machines begin to learn from their own invented content, the foundations threaten to crumble. The industry is facing a turning point that goes far beyond technical issues.
Whether text creation, language translation, email management or media production, AI has long been part of everyday life for many people. A study by the University of Zurich shows that more than half of the population already uses tools such as ChatGPT or Gemini. The younger generation in particular is open to the new technology.
High economic potential with risks
In companies, AI is seen as a driver of rationalisation. Studies forecast potential worth billions for the Swiss economy. Especially in the areas of pharmaceuticals, logistics, education and software development, experts are expecting profound efficiency gains. However, the euphoria is clouded by the first warning signs.
Dubious content instead of real information
In the journalistic and media environment, AI is already being used for the mass production of content, from sports reports to financial data. However, in many cases, meaningfulness, source clarity and factual accuracy fall by the wayside. Europol warned as early as 2023 that up to 90% of online content could be synthetic by 2026.
When machines learn from machines
A central problem is that AI models are based on existing data. However, these are increasingly AI-generated themselves. The result is a self-reinforcing feedback loop in which quality and factual accuracy drop rapidly. If models are trained with synthetic data, the results deteriorate drastically, even to the point of complete system collapse.
The Grok case and the ethical dimension
A recent incident shows just how dangerous this development can be. The chatbot “Grok” developed by Elon Musk recently disseminated anti-Semitic content and praised Adolf Hitler. The cause is presumably uncontrolled training on manipulated or synthetic content. The case illustrates how urgently rules for data validation, ethical guidelines and quality standards are needed.
Synthetic data is no substitute for reality
Synthetically generated data only depicts historical patterns and can perpetuate or reinforce existing biases. Without new, high-quality training data, further development will come to a standstill. At the same time, the handling of sensitive or manipulative content raises fundamental questions about responsibility, transparency and regulation.