Why we need technology economists

Organizations need technology economists, not just IT finance professionals, to evaluate AI investments because AI breaks the stable relationship between technology spending and business outcomes, according to a Substack article by Howard A. Rubin. The article argues that a $10 million AI investment could generate $100 million in value or nothing at all, and that technology economists study entire economic systems and value chains, focusing on capital allocation efficiency, opportunity cost, and portfolio optimization, which traditional IT finance overlooks.

The advent of AI is precisely why organizations need technology economists, not just IT finance professionals. IT finance is primarily concerned with budgeting, accounting, cost allocation, depreciation, chargebacks and financial reporting. These disciplines remain important, but they assume a relatively stable relationship between technology spending and business outcomes. AI breaks that assumption https://howardarubin.substack.com/p/why-ai-roi-is-so-darn-hard-to-measurehttps:/howardarubin.substack.com/p/why-ai-roi-is-so-darn-hard-to-measurehttps:/howardarubin.substack.com/p/why-ai-roi-is-so-darn-hard-to-measure . Technology economics asks a fundamentally different question: How do technology investments create, destroy, shift or delay economic value? AI introduces a set of economic dynamics that traditional IT finance was never designed to evaluate. In traditional IT, spending $10 million typically produced a somewhat predictable capacity increase or operational improvement. With AI, a $10 million investment might generate $100 million in value. It might generate no value at all. It could increase costs while appearing successful. It could also create strategic advantages that do not show up in financial statements for years. A technology economist studies the relationship between technology inputs, organizational capability, productivity outcomes and economic value creation. IT finance largely records the spending. AI is not merely another technology platform. It acts as a form of digital labor. Organizations now face questions such as: These are economic questions, not accounting questions. A fascinating paradox https://www.northerntrust.com/united-states/insights-research/2026/investment-perspective/is-ai-inflationary-or-deflationary is emerging: AI can reduce costs in some areas while dramatically increasing costs elsewhere. For example, fewer coding hours. More GPU costs. Lower service desk costs. Higher cybersecurity costs. Reduced consulting expenses. Increased data management expenses. Technology economists study entire economic systems and value chains. IT finance often sees only line items. Historically, organizations measured projects delivered, systems implemented, budgets achieved and uptime percentages. The AI era requires measuring: Technology economists focus on these outcome measures. This is one reason why AI performance measurement frameworks https://www.nytimes.com/2026/08/03/business/economy/ai-spending-tokenomics.html , including AI-focused balanced scorecard approaches, are becoming increasingly important. One of the largest AI risks is not technological failure. It is investing in the wrong AI initiatives. A bank might spend $50 million building an AI solution that saves $5 million annually while ignoring another opportunity that could have generated $500 million in new revenue. Technology economics focuses on capital allocation efficiency, opportunity cost, marginal returns and portfolio optimization. These concepts sit outside traditional IT finance. Historically, technology supported the business. Increasingly, technology is the business. In many industries, AI determines customer experience, operating efficiency, innovation speed and competitive advantage. Technology is becoming a primary production factor alongside labor, capital and natural resources. Organizations therefore need experts who understand the economics of technology as a production asset. Many organizations are deploying AI rapidly without understanding: A technology economist examines the total lifecycle economics. The cheapest AI solution today may become the most expensive solution over the next decade. The central challenge of the AI era is no longer “Can we build it?” The challenge is, “Should we build it, where should we deploy it, what value will it create, what risks will it introduce and what is the optimal economic allocation of technology capital?” Those are technology economics questions. IT finance professionals are essential for controlling and reporting technology spending. Technology economists are essential for determining whether that spending creates sustainable economic value. As AI becomes embedded into every business process, the organizations that outperform will not necessarily be those with the biggest AI budgets. They will be those that best understand the economics of technology itself — how AI, data, infrastructure, labor, risk and innovation combine to create measurable business value https://www.cio.com/article/4137420/5-metrics-to-drive-successful-ai-outcomes.html . That is the domain of technology economics.