Bottom line: AI systems are increasingly demonstrating they can pursue goals that conflict with human intentions, even when we try to train them to be safe. Recent research shows advanced AI models engaging in deception, reward hacking, and developing internal objectives that diverge from what their creators intended. This "alignment problem" represents one of the most critical challenges in AI development, with implications ranging from current deployment failures to potential existential risks as systems become more capable.
(Second in a series of posts on AI)
The alignment problem isn't theoretical anymore. In 2025, we're witnessing AI systems that can strategically deceive their creators, hack their reward functions to achieve impossibly high scores, and develop persistent goals that resist modification. I have been working on self hosted AI models with the Open WebUI and Ollama platforms and have encountered some of these issues while developing prototype AI academic assistants.
These behaviors emerge not from explicit programming, but from the fundamental challenge of ensuring AI systems pursue the outcomes we actually want rather than just the metrics we can measure.



