La inversión en IA cae por problemas de financiación y propiedad intelectual
The artificial intelligence sector is experiencing a marked slowdown in funding, far from the peaks recorded during the pandemic. The combination of high interest rates, high development costs, and technical complexities has deterred investors, who now prioritize profitability over excessive growth. This caution has led to a significant decline in both mergers and acquisitions and venture capital rounds, reflecting a cyclical adjustment in which only the most robust and established companies are able to attract resources, while many smaller startups will either disappear or be acquired. This transition toward a more mature and integrated market partly explains the reduction in activity, as AI is now embedded within general-purpose software rather than functioning as standalone, attractive categories for quick acquisitions. Investors are demanding sustainable business models and tangible revenue generation—a particular challenge for the nascent generative AI landscape. As a result, capital flows are consolidating around established key players, marking the end of the era of unlimited expansion based solely on technological potential without immediate commercial validation. This report is relevant to the field of open data because it underscores the critical need for high-quality, verified data to build profitable and ethical AI models. Capital constraints force companies to optimize their resources, making access to open, transparent, and well-structured datasets a strategic competitive advantage. Furthermore, the pressure for economic sustainability fosters collaboration and the sharing of data infrastructures, aligning commercial interests with the principles of accessibility and transparency that define the philosophy of open data.
Source: revistaeyn.comPublished on 2024-01-10