For years, single large language models (LLMs) have reigned supreme in the AI world. But a new challenger has emerged: multi-agent systems, where multiple LLMs work together like a team. This collaborative approach holds the promise of overcoming some of the limitations of individual LLMs, particularly in tasks requiring complex reasoning. Think of it like a group of experts brainstorming – different perspectives and specialized knowledge can lead to more creative and effective problem-solving. However, recent research reveals a surprising twist. While these AI teams excel at complex tasks like strategic planning and ethical decision-making, they sometimes stumble on seemingly simple tasks like translation. This intriguing phenomenon, termed "problem drift," occurs when the ongoing discussion within the AI team leads them astray from the straightforward solution. Imagine a team of translators getting so caught up in debating nuances that they lose sight of the original text's meaning. This highlights a key challenge: finding the right balance between leveraging the collective intelligence of multiple LLMs and maintaining focus on the task at hand. The research also sheds light on how these AI teams adapt to different problem difficulties. They spend more time discussing challenging problems, dynamically adjusting their effort based on the complexity. This adaptability is a significant advantage, but it also introduces new challenges, such as "alignment collapse." This occurs when extended discussions cause the AI team's ethical reasoning to deteriorate, raising crucial questions about AI safety. In the quest to create more robust and capable AI, the future likely lies in hybrid approaches that combine the strengths of individual and collaborative models. By understanding the dynamics of AI teamwork, including both its potential and pitfalls, we can harness the full power of collaborative intelligence to tackle the complex challenges of tomorrow.