Core Insights - The MIT NANDA project released a report on the state of commercial AI in 2025, revealing that despite $40 billion in investments in generative AI, 95% of organizations see almost zero actual returns [2][3] - A paradox exists where AI technology is advancing rapidly, yet employees prefer using personal AI tools like ChatGPT over corporate solutions, leading to a significant "shadow AI economy" [2][6] - The report indicates a fundamental shift in the logic of AI entrepreneurship, emphasizing that success now hinges on the ability of AI to continuously learn and evolve in real business scenarios [2][8] Old Logic Breakdown - The report highlights that 95% of investments in AI have not yielded returns, prompting a reevaluation of past approaches [3] - Many companies treat AI as a plug-and-play tool, failing to recognize that AI requires ongoing learning and adaptation, similar to an expert [3][6] - The preference for general-purpose models over task-specific AI has become evident, with only 5% of companies successfully implementing specialized AI after initial interest [3][6] New Logic Emergence - A small percentage of companies (5%) have successfully adapted their approach to AI, treating it as an external expert that grows alongside the organization [7][8] - Successful organizations focus on continuous learning mechanisms, integrating feedback loops into their AI systems to enhance adaptability [8][9] - The shift from traditional software licensing to "growth services" reflects a new business model where companies invest in AI's potential for ongoing improvement [7][8] Future Competitive Landscape - The report suggests that the future of AI entrepreneurship will prioritize understanding specific scenarios over broad technological capabilities [10][11] - Companies are encouraged to rethink their ROI perspectives, as "cost-saving AI" often provides more measurable returns than growth-oriented AI [10][11] - The rise of online learning capabilities is expected to disrupt the AI market, favoring those who can adapt quickly to specific use cases and continuously optimize their models [11]