Part 2/8:
Despite the initial enthusiasm surrounding AI, experts predict that progress for major companies may become more challenging. The hypothesis suggests that continued improvement in AI performance requires increased amounts of data and computational resources. As expenses soar and the efficiency gains reduce, the industry finds itself at a crossroads.
According to analysts, the "low hanging fruit" of AI advancements has been largely picked. Leaders in the AI sector, such as OpenAI, Anthropic, and Google, are currently facing hurdles in scaling their models effectively. As the cost of training AI models skyrockets, enterprises are forced to evaluate the trade-offs between performance improvements and investment returns.