Why Specialization Is Inevitable
As AI systems grow more powerful, organizations increasingly need tailored tools for specific tasks rather than generalist models.
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Specialization in AI systems is inevitable because of fundamental constraints discovered across multiple fields. Optimization theory, evolutionary biology, and competitive markets all independently show that when resources are finite, systems perform better by concentrating on specific tasks rather than attempting broad generality. In machine learning practice, this pattern appears repeatedly: systems that achieve breakthrough results tend to be narrowly focused on single domains, and training systems on multiple tasks simultaneously can actually degrade performance on individual tasks through what is called negative transfer.
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