What happened
The AI sector is experiencing a boom, driven by strong profitability, self-funded investment, and real-world productivity gains, with core companies like Alphabet, Nvidia, and Microsoft generating substantial revenue. This growth is underpinned by significant investment in AI infrastructure, including chips and data centres. However, concerns persist regarding potential artificial valuation inflation from circular investments and the future exhaustion of high-quality data for model training.
Why it matters
The reliance on potentially unsustainable assumptions and the prospective exhaustion of high-quality training data introduce significant operational constraints. This raises due diligence requirements for procurement and strategic planning teams in evaluating AI solution longevity and vendor stability. Furthermore, the potential for data exhaustion creates an accountability gap for AI development and data governance teams, increasing the oversight burden for securing novel, high-quality datasets to maintain model efficacy and avoid performance degradation.
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