Smart Tax once tried to measure AI intelligence, but modern systems started thinking differently, quietly changing how automation really works today.

AI is no longer doing one task at a time, it now plans, checks results, and improves itself automatically.  

Developers moved from single prompts to intelligent systems that repeat steps until goals are reached without human interference.

This looping behavior makes AI more accurate, cheaper, and reliable compared to older one-time response models everyone used earlier.  

Faster, lightweight AI models encouraged systems to think in small steps instead of producing long answers at once.

Smart tax ideas failed because intelligence is no longer linear; it works like a cycle that improves quietly.  

Businesses noticed they could get better results without paying more just by designing smarter AI workflows.

Policymakers still count AI usage in simple units, but loop-based systems hide real activity inside processes.  

AI agents now focus on outcomes, not actions, which breaks old pricing, control, and measurement systems completely.

The future of AI belongs to adaptive systems that learn, repeat, and improve without following outdated rules.