Amplification of Advantage

Amplification of advantage is the specific predictive claim Toyama's framework makes about AI in a world of unequal foundations. When the same tool is available to users with radically different levels of educational preparation, institutional support, market access, and cultural capital, the tool's amplification factor operates on those differences. A twenty-fold multiplier on a deep foundation produces much more output than the same multiplier on a shallow foundation — not because the tool is unfair but because multiplication of unequal quantities yields unequal results. The prediction is not that AI will fail to benefit those with weaker foundations; it is that AI will benefit them less than it benefits those with stronger foundations, widening the absolute gap even as both groups experience genuine gains.