Taiwan Central Bank Warns of AI Investment Bubble and Risks to Global Employment
Taiwan’s central bank included artificial intelligence among the five major risks to the global economy for the first time in its Financial Stability Report. The bank highlighted the vast sums companies are investing in computing capacity, data centers and model development. If the expected productivity and profits fail to materialize, valuation corrections could spread to equities, credit markets and financial institutions. AI adoption could also intensify layoffs and job displacement.
The latest report said the main risk from the AI boom is that weaker-than-expected investment returns could cause the bubble to burst, with geopolitical and other factors potentially compounding the fallout. The central bank did not disclose total AI investment, the number of jobs that could be affected or a specific timeframe. The only confirmed quantitative detail is that it identified five major risks to the global economy.
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The history behind this eventBIS Warns AI Boom Could Derail Monetary Policy
The artificial-intelligence investment boom is reshaping both sides of the global economy. Spending on data centers, advanced chips and cloud infrastructure is lifting demand, trade and asset prices, while eventual productivity gains could expand supply and restrain inflation. The Bank for International Settlements said those forces differ in timing and across countries, obscuring conventional signals such as economic slack, the natural interest rate and underlying price pressure. That uncertainty matters because central banks set policy against estimates that cannot be observed directly.
In BIS Bulletin No. 130, published July 28, 2026, the institution said data-center and related IT investment had risen to 0.8% of GDP in the United States and more than 1% in Australia. Industry estimates put global AI-related investment at about $500 billion today, potentially reaching $3 trillion to $4 trillion by 2030. The BIS warned that overestimating supply gains or underestimating demand could leave policy too loose, while the opposite error could produce unnecessarily restrictive conditions; it favored a gradual, data-dependent approach.
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