What Might Remain After the AI Bubble
The Infrastructure of Dependency
Novelty fades faster than infrastructure. What felt revolutionary in 2023 had become routine by 2025. Three out of four knowledge workers used AI tools daily, and nearly half had adopted them within months [1]. The excitement of early experimentation settled into expectation.
Automation ceased to feel like transformation and became background condition. Documents generated themselves. Code suggestions appeared without prompting. Customer service inquiries routed through systems that rarely required human hands. Reports warned that while automation might displace millions of roles, it would also create new ones [2]. Yet the framing obscured a deeper truth: the shift was not replacement but reconfiguration. Work reorganized itself around new definitions of human attention.
Automation normalized first in repetitive domains. Clerical work, data entry, and retail transactions became quieter, thinner. Fewer openings appeared each year, but the change rarely made headlines [3]. Trust, however, lagged behind adoption. Surveys in 2025 showed that less than half of users trusted AI systems, yet most continued to rely on them [4,5]. Dependency did not wait for confidence.
Understanding changed as judgment was delegated. Many organizations identified explainability as a key risk, yet few acted on it [6]. The question "how did it reach that conclusion" grew less urgent than "does the conclusion work." Algorithms handled loans, diagnoses, and hiring; humans approved results they no longer fully grasped.
These shifts will outlast the current investment wave. Data centers will keep drawing power; code will remain embedded in workflows because removal costs more than upkeep. Once automation becomes infrastructure, reversal requires justification. People must prove why their judgment adds value. Dependency deepens through erosion of alternatives. Skills fade, training pipelines close, and human redundancy becomes quiet habit [7,8].
The AI surge will leave its outline in expectations and habits. Workers will assume assistance as default, students will prompt rather than research, and creative professionals will design with algorithms as standard practice. These changes will not feel dramatic because they accumulate gradually, until the old way of working feels distant and slow.
What automation replaces is less important than what it makes irreplaceable. When repetition disappears, patience fades. When instant answers abound, curiosity dulls. Trust may remain partial, yet dependence will harden. The question is not what AI removes, but what capacities weaken when the act of thinking itself is outsourced. These are the quiet dependencies now forming what the future will call normal.
References
[1] Microsoft. Work Trend Index: AI at Work Is Here. Now Comes the Hard Part. May 2024.
https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
[2] AIPRM. AI Replacing Jobs Statistics: Will AI Take My Job? 2024.
https://www.aiprm.com/ai-replacing-jobs-statistics/
[3] National University. AI Job Statistics: What the Data Says About AI and Your Career. January 2025.
https://www.nu.edu/blog/ai-job-statistics/
[4] KPMG. Trust in AI Study: Global Perspectives. 2025.
https://mbs.edu/news/global-study-reveals-trust-of-ai-remains-a-critical-challenge
[5] KPMG. Trust in AI 2025: A U.S. Perspective. January 2025.
https://kpmg.com/us/en/media/news/trust-in-ai-2025.html
[6] McKinsey & Company. Building AI Trust: The Key Role of Explainability. 2024.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/building-ai-trust-the-key-role-of-explainability
[7] Organisation for Economic Co-operation and Development. AI and the Future of Work. 2024.
https://www.oecd.org/future-of-work/AI-and-the-future-of-work/
[8] Srnicek, Nick. Platform Capitalism. Polity Press, 2017.
[9] Hayles, N. Katherine. Unthought: The Power of the Cognitive Nonconscious. University of Chicago Press, 2017.
All sources accessed and verified as of October 2025.
