Leading artificial intelligence companies, including OpenAI, Google, and Anthropic, are reportedly encountering diminishing returns in their efforts to develop more advanced large language models (LLMs). OpenAI's upcoming model, known internally as Orion, has shown minimal performance gains over previous GPT iterations, signaling a potential plateau in pre-training advancements. Ilya Sutskever, OpenAI co-founder, acknowledged that scaling up pre-training has plateaued, prompting a shift in strategies among major AI labs. Google's Gemini AI model has not achieved the desired performance gains despite increased computing power and training data, indicating challenges in their scaling efforts. Anthropic is planning to release Claude 3.5 Opus but is also facing similar limitations. Margaret Mitchell, chief ethics scientist at Hugging Face, noted that "the AGI bubble is bursting a little bit," highlighting industry-wide recognition of these challenges. In response, companies are now exploring new methodologies beyond simply increasing model sizes, such as focusing on test-time compute and improving inference techniques. This shift signifies a significant change in the AI landscape as the industry moves away from the "bigger is better" approach that dominated the previous decade.