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VOL. 10, ISSUE 2 (2026)
The Koyal’s Egg Fallacy: AI deployment outpacing understanding, governance, and interpretability
Authors
Abhijeet Parshuram Salvi, Aaditya Arvind Singh, Sneha Kumari
Abstract
The rapid embedding of AI systems into
healthcare, law, finance, and governance has outpaced our capacity to
understand, monitor, or predict their behaviour—a structural mismatch this
paper terms the “Koyal’s Egg Fallacy,” after the cuckoo that lays undetected
eggs in another bird’s nest. This paper makes four documented contributions:
(i) empirical evidence that AI failures—hallucinations, silent degradation, and
high-stakes errors—are the statistical norm rather than the exception, with 91%
of deployed models degrading in production and 75% of organizations lacking
monitoring [5]; (ii) a systematic analysis of the mechanistic
interpretability gap showing that current techniques are fundamentally
non-scalable to billion-parameter production systems [3]; (iii) a
complex-systems analysis demonstrating that multi-model interactions generate
cascading failures undetectable at the component level, illustrated by the
still-unexplained 2010 Flash Crash [4, 6, 7]; and (iv) a
governance-deployment gap framework quantifying the asymmetry between
exponential infrastructure growth (+115% AI workload capacity) and stagnant
governance maturity (monitoring coverage: 25%, static). The paper argues that
the structural mismatch between deployment pace and understanding represents a
form of collective epistemic overconfidence—we design safeguards for mechanism
A while system may operate via mechanism B. It calls for epistemic humility,
mandatory monitoring before deployment, and investment in scalable
interpretability before further expansion of AI into critical decision-making
domains.
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Pages:69-72
How to cite this article:
Abhijeet Parshuram Salvi, Aaditya Arvind Singh, Sneha Kumari "The Koyal’s Egg Fallacy: AI deployment outpacing understanding, governance, and interpretability". International Journal of Advanced Engineering and Technology, Vol 10, Issue 2, 2026, Pages 69-72
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