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exceptionalism," demonstrating that software companies are not immune to standard
macroeconomic cycles despite their unique business models. Similar to traditional
industries, tech firms undergo massive workforce reductions in response to rising interest
rates (Bernanke & Blinder, 1992), normalizing post-pandemic demand (Brynjolfsson et al.,
2020), and the unsustainability of "growth-at-any-cost" models (Gompers et al., 2020),
though their reactions may be delayed by venture capital funding cycles
5. Conclusions and recommendations
This study documents the global technology layoff wave from March 2020 to April
2025, revealing a significant labor market disruption of over 808,000 job losses across
2,863 companies. The findings challenge the narrative of technology as an industry
immune to economic cycles, instead demonstrating its vulnerability to macroeconomic
forces identified by Bernanke and Blinder (1992). The data reveals a “bullwhip effect” in
employment markets, where hiring and firing decisions overshot equilibrium following
the pandemic’s initial digital acceleration, leading to a concentrated spike in layoffs three
years after the initial shock—a delay that validates the supply chain amplification
mechanisms described by Lee et al. (1997).
Ultimately, the findings indicate a paradigm shift: tech layoffs are increasingly
utilized as a strategic mechanism to free up capital for heavy investments in generative AI
infrastructure, signaling an accelerated phase of creative destruction (Acemoglu &
Restrepo, 2019). For policymakers and practitioners, the study emphasizes the need for
countercyclical funding, regional adjustment programs, and more sophisticated workforce
planning to mitigate the volatility of innovation-intensive sectors. By balancing labor
market flexibility with worker protection, stakeholders can better align with sustainable
development goals (SDG 8 and 9) in an era where technological change operates with
unprecedented speed and global reach.
References
[1]. Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How
technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30.
[2]. Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent
technological change: An empirical exploration. Quarterly Journal of Economics, 118(4),
1279–1333.
[3]. Bernanke, B. S., & Blinder, A. S. (1992). The federal funds rate and the channels
of monetary transmission. American Economic Review, 82(4), 901–921.
[4]. Brynjolfsson, E., Horton, J. J., Ozimek, A., Rock, D., Sharma, G., & TuYe, H. Y.
(2020). COVID-19 and remote work: An early look at US data. National Bureau of
Economic Research Working Paper No. 27344.
[5]. Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton
University Press.
[6]. Glaeser, E. L., & Gottlieb, J. D. (2009). The wealth of cities: Agglomeration
economies and spatial equilibrium in the United States. Journal of Economic Literature,
47(4), 983–1028.
[7]. Gompers, P., Gornall, W., Kaplan, S. N., & Strebulaev, I. A. (2020). How do
venture capitalists make decisions? Journal of Financial Economics, 135(1), 169–190.
[8]. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply
chains. Sloan Management Review, 38(3), 93–102.
[9]. Schumpeter, J. A. (1942). Capitalism, socialism and democracy. Harper &
Brothers.

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