In his contribution to Google Cloud's 'Future of AI: Perspectives for Startups 2025' report, Yoav argues against the concept of Artificial General Intelligence (AGI) as a distraction, advocating for a more grounded discussion on AI's current capabilities, particularly in the enterprise sector where adoption is slower despite heavy experimentation. He identifies two main challenges hindering large-scale AI deployment: the high cost and inefficiency of large language models (LLMs), and their inherent probabilistic nature leading to inconsistent outputs. Yoav suggests that smaller models and alternative architectures, like AI21's Jamba models, could address cost issues, while a more integrated AI system could improve reliability by combining LLMs with other technologies. He advises startups to focus on achieving "product-algo fit" by understanding AI's strengths and weaknesses to create products that optimize AI capabilities while mitigating its limitations.