The limits of raw computing power in AI

As the global race for AI accelerates, a growing number of experts are questioning whether simply adding more computing power still delivers meaningful results. In a recent blog post, digital policy expert Jovan Kurbalija argues that AI development is approaching a critical plateau, where massive investments in hardware produce only marginal gains in performance.

Despite the dominance of advanced GPUs and ever-larger data centres, improvements in accuracy and reasoning among leading models are slowing, exposing what he describes as an emerging ‘AI Pareto paradox’.

According to Kurbalija, the imbalance is striking: around 80% of AI investment is currently spent on computing infrastructure, yet it accounts for only a fraction of real-world impact. As hardware becomes cheaper and more widely available, he suggests it is no longer the decisive factor.

Instead, the next phase of AI progress will depend on how effectively organisations integrate human knowledge, skills, and processes into AI systems.

That shift places people, not machines, at the centre of AI transformation. Kurbalija highlights the limits of traditional training approaches and points to new models of learning that focus on hands-on development and deep understanding of data.

Building a simple AI tool may now take minutes, but turning it into a reliable, high-precision system requires sustained human effort, from refining data to rethinking internal workflows.

Looking ahead to 2026, the message is clear. Success in AI will not be defined by who owns the most powerful chips, but by who invests most wisely in people.

As Kurbalija concludes, organisations that treat AI as a skill to be cultivated, rather than a product to be purchased, are far more likely to see lasting benefits from the technology.

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