Artificial intelligence, though often unnoticed, is embedded in many aspects of daily life, from search engines and smart speakers to email filters. These AI systems are specialized and operate within specific domains, unlike the proposed concept of artificial general intelligence (AGI), which aims to perform any intellectual task a human can do. DeepMind scientists suggest in their paper "Reward is Enough" that maximizing rewards in complex environments could lead to the development of AGI, as this process forces the learning of complex skills. To illustrate, the paper uses examples like the AlphaGo system, which, through reinforcement learning, developed advanced strategies to win at the game of Go. The paper posits that by interacting with complex environments, agents can acquire abilities such as perception, social intelligence, and planning. While the hypothesis is primarily philosophical, it emphasizes the potential of reward maximization to cultivate skills necessary for AGI, although this has yet to be tested in real-world complexities.