Artificial Intelligence (AI) has impressive breadth. But breadth of capability is not yet proof of economy-wide transformation.
Several uncertainties make the classification premature or incomplete.
What is currently referred to as AI is not one technology. AI includes language models, predictive systems, robotics, computer vision, recommendation engines, and other technologies with different adoption patterns.
Capability does not equal reliability. Models can perform well in demonstrations yet fail under unfamiliar, messy, or high-stakes conditions. Benchmark progress may exaggerate dependable real-world performance.
Adoption is not transformation. Purchasing AI tools or using chatbots does not prove sustained productivity. Many organizations remain stuck between experimentation and workflow integration.
Complementary systems are immature. Reliable value still depends on good data, redesigned processes, skilled users, integration, governance, and trust.
Diffusion is not equally distributed. Access, infrastructure, expertise, and benefits remain concentrated across firms, sectors, and countries.
The long-term evidence is incomplete. General-purpose technologies are ultimately recognized through sustained productivity, widespread complementary innovation, and institutional change not through technical potential alone.
AI has the characteristics of an emerging general-purpose technology, but it has not yet completed the journey from broad capability to broad transformation.
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