Howard Rubin: Why Tech Spending Benchmarks Often Mislead Operators
A persistent structural challenge highlighted in this episode is the disconnect between technology investment and demonstrable business outcomes, which fuels operational inefficiency and accountability gaps in technology spending. As articulated by technology economist Dr. Howard Rubin, a common industry tendency is to measure IT success based on technology adoption or budget size rather than objective business results. This pattern is not limited to large enterprises but affects small and mid-sized organizations, many of which feel compelled to maintain “current” technology without clear evidence of operational or financial return.
Primary evidence centers on the inadequacy of current macroeconomic indicators—such as the Consumer Price Index (CPI) and Gross Domestic Product (GDP)—for assessing technology value and risk in smaller organizations. Dr. Rubin noted that official statistics and classic economic telemetry do not track the true inflation or productivity impact of technology stacks, particularly as hyperscalers invest trillions in infrastructure. The transcript highlights that price increases or capital recovery pressures in services like Microsoft Office or cloud platforms are likely to affect smaller organizations first, exacerbating operational risk and cost unpredictability.
Supporting developments include analysis of flawed benchmarking practices, such as using IT spend as a fixed ratio to revenue or operating expense without examining enabling value or efficiency outcomes. Failure to contextualize technology investments can lead to counterproductive decisions, like arbitrary cost-cutting when IT as a percentage of expenses rises, ignoring possible operational savings or revenue lift driven by technology. Dr. Rubin advocates for pattern recognition and bespoke analysis over reliance on aggregated industry numbers, pointing out that mass market vendor investments and macroeconomic policy often obscure direct impacts at the SMB and MSP level.
For MSPs and technology decision-makers, the operational implication is a heightened need to create internal technology inflation indices and track category-specific price pressures. Rather than relying on aggregate industry benchmarks or public economic data, service providers should establish tailored metrics to capture their own cost structures, labor pressures, and technology value. The discussion points toward the need for more deliberate accountability and ongoing evaluation—especially given that upstream price increases from hyperscalers and SaaS vendors are set to impact providers and their clients, with limited ability to negotiate at smaller scale.
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