The End of AI Exponentiation: Fluttering Inside and Outside AI Bubble
The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large language models (LLMs), large-scale compute infrastructures, and autonomous reasoning systems. However, the rapid acceleration of AI has increasingly shown technological, societal, economic, ethical and infrastructural challenges associated with peak data limitations, rising computational demands, synthetic data recursion, valuation inflation, and societal instability. The traditional scaling paradigms that have powered the modern AI systems are gradually encountering friction in sustaining continuous exponential growth. This paper views ``the ...
| Authors | Victor Kebande |
|---|---|
| Primary category | cs.AI |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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