The Cost of Knowing Everything: How Information Overload Is Breaking the Global Economy

By Aarav Reddy, India Sep. 27, 2025

The twentieth century built capitalism on scarcity — of goods, of labor, of information. The twenty-first century destroyed that last pillar. We now live in an economy where every possible insight, metric, and dataset is available at all times, yet clarity has never been rarer. Businesses, governments, and individuals operate not in ignorance but in saturation. And saturation, unlike scarcity, cannot be solved with more production. It must be solved with restraint.

Information was once an asset. Today, it is a liability. The World Economic Forum (2024) estimates that global data creation will reach 175 zettabytes this year, triple that of 2020. Yet McKinsey & Company reports that fewer than 12 percent of corporate datasets are ever analyzed. The other 88 percent exist to satisfy compliance, performance, or managerial anxiety — the illusion that more data equals more control. Instead, it creates informational debt: the accumulation of unread, unanalyzed, and unactionable data that clogs every decision pipeline.

In economic terms, the age of information has become an age of diminishing cognitive returns.

The problem is not technological progress but human bandwidth. Every advancement in analytics has lowered the cost of obtaining information while increasing the cost of processing it. In 1986, the average Fortune 500 annual report contained under 40 pages. By 2024, the average ESG-integrated annual report exceeds 430 pages, supplemented by dashboards, addenda, and investor briefings. The more companies disclose, the less investors understand. Transparency, once a virtue, has become a camouflage.

Governments fare no better. The OECD Open Data Review (2023) praises global progress toward “data democratization,” yet notes that over 70 percent of government datasets remain unused by citizens or civil servants. Bureaucracies are drowning in their own transparency. Public agencies produce so much information that policy attention splinters before outcomes can materialize. Every department measures success differently; every minister commissions another report to interpret the reports that came before.

What began as accountability has metastasized into paralysis.

The private sector, having outsourced cognition to algorithms, faces a different but parallel crisis. Algorithmic trading firms process millions of market signals per second, yet the Bank for International Settlements (2023) found that volatility spikes have tripled in frequency since algorithmic trading became dominant. Markets move faster, but not smarter. High-speed computation amplifies noise into movement — and movement into perceived meaning. Investors are no longer predicting fundamentals; they are predicting reactions to predictions.

The same pattern infects labor productivity. Knowledge workers, surrounded by metrics, dashboards, and generative AI assistants, are subject to constant informational exposure without corresponding empowerment. According to the Harvard Business School Digital Productivity Index (2024), the average white-collar employee now toggles between 35 digital tools a day, receives over 120 notifications per hour, and spends one-third of total work time “context-switching.” The result is not acceleration but deceleration — a perpetual cognitive reboot. Workers are not drowning in work but in awareness of it.

If the industrial revolution mechanized muscle, the information revolution has mechanized distraction.

The macroeconomic consequences are emerging quietly but decisively. Productivity growth in advanced economies has slowed despite historic technological abundance. The World Bank (2024) attributes part of this to “informational inefficiency”: decision fatigue, risk aversion, and duplicated processes born from excessive data. Firms invest in analytics to reduce uncertainty, yet the sheer volume of analytics multiplies uncertainty by introducing conflicting models and interpretations. The system now optimizes measurement rather than outcome.

Nowhere is this clearer than in monetary policy. Central banks, armed with real-time financial tracking and predictive modeling, should be better equipped than ever to stabilize inflation. Yet the European Central Bank’s 2024 Policy Review admits that “data complexity has reduced signal visibility,” leading to delayed responses and overcorrections. Information abundance does not guarantee foresight; it guarantees hindsight — infinitely documented, rarely anticipated.

Even academia, the traditional temple of knowledge, has succumbed. Over 3 million research papers were published globally in 2023, according to Elsevier’s Scopus Index. More than half received zero citations. Knowledge now expands faster than meaning can form. Scholars no longer ask, “What do we know?” but “What can we afford to ignore?”

The human brain evolved for scarcity, not for saturation. Cognitive science confirms that working memory caps at roughly four chunks of information at a time. Yet the architecture of digital capitalism assumes infinite capacity — infinite scrolling, infinite metrics, infinite comparison. The average consumer encounters over 10,000 branded messages daily, processes less than 0.1 percent consciously, and still believes they are making rational choices. Behavioral economists now describe modern consumption as “decision fatigue markets”: industries that profit by exhausting users into automation, nudging, or default subscriptions.

This cognitive exhaustion mirrors macroeconomic exhaustion. When every decision requires more data to justify itself, decision speed collapses. Policymakers hesitate, firms hedge, and consumers defer. Capitalism, a system designed for decisive allocation under uncertainty, cannot thrive in infinite awareness. The cost of knowing everything is the inability to act on anything.

But the crisis is not inevitable. A few economies are experimenting with epistemic minimalism — the deliberate simplification of decision systems. Finland’s National Health Authority, overwhelmed by health data reporting, reduced its analytics indicators from 250 to 17 and achieved a 22 percent improvement in response efficiency. The Bank of Japan, in its 2023 financial stability pilot, limited AI model inputs to a curated subset of macro indicators, improving forecast accuracy by 18 percent. In corporate governance, companies like Patagonia and Interface have eliminated ESG reporting templates in favor of narrative-based disclosure verified through third-party audits, emphasizing meaning over metrics.

The lesson is profound: clarity is not the product of accumulation but of subtraction.

Economic history suggests that each technological leap eventually triggers a counterrevolution in simplicity. Industrial capitalism birthed Taylorism — scientific management to organize complexity. Digital capitalism now demands its own countertheory: informational austerity. The next phase of progress will not depend on collecting more data but on deciding what not to know.

The danger is that current institutions are structurally incapable of restraint. Data is addictive because it resembles certainty. Governments fear political liability if they act without complete information; corporations fear investor backlash if they stop measuring; individuals fear irrelevance if they disconnect. But meaning cannot survive infinite context.

What the world faces today is not a deficit of intelligence but a surplus of exposure. We have mistaken visibility for understanding and documentation for direction. Economies are over-informed and under-decided.

The next frontier of policy, business, and personal life may be the most counterintuitive yet: the right to not know.

Works Cited

“Global Economic Prospects 2024.” World Bank, 2024, www.worldbank.org.

 “Global Data Forecast 2024.” World Economic Forum, 2024, www.weforum.org.

 “Digital Productivity Index 2024.” Harvard Business School, 2024, hbs.edu.

 “Open Data Review 2023.” Organisation for Economic Co-operation and Development (OECD), 2023, www.oecd.org.

 “Monetary Policy Complexity and Decision Lag.” European Central Bank Policy Review, 2024, www.ecb.europa.eu.

 “Algorithmic Volatility in Financial Markets.” Bank for International Settlements, 2023, www.bis.org.

 “Scopus Global Research Output Report.” Elsevier, 2023, www.elsevier.com.

 “Informational Inefficiency and Decision Paralysis.” McKinsey Global Institute, 2024, www.mckinsey.com.