At AINNA, we review the books of Malaysian PKS who bought into the AI hype and now face a different reality. Once token usage, GPU time, cloud hosting and maintenance are booked as recurring opex rather than pilot experiments, many leadership teams slow or stop deployment because the numbers no longer justify the spend.
The real misallocation is defaulting to LLM inference for every workflow. Predictable, low-complexity tasks should sit on parsers, rule-based automation or detached systems where unit costs are fixed and auditable. AI should only draw budget when the measurable return clearly exceeds the variable cost.
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