I think you may be conflating the manner in which a tool is used with the tool itself. Incentives and biases are irrelevant to the scaling paradigm of the trans

I think you may be conflating the manner in which a tool is used with the tool itself. Incentives and biases are irrelevant to the scaling paradigm of the transformer architecture, for example. I don't think there's a single valid example of a biased or racist architecture as such. An algorithm can be seen as a particular use of an architecture, and like every human endeavor can be done well or badly. The infamous tank detector neural network was biased towards clouds. Microsoft Tay was biased towards troll induced garbage. Neither bias says anything about the architectures underlying the implementation except that the tool was used poorly. I think we should leave the discussions of incentive and bias at the level of particular implementation, as abstract architectures can't be generically adjusted or affected by biases or ethics or moral considerations. The selection of training data and intended functionality and particulars of a project are where biases and other considerations arrive on the scene. The ideas of transformer or other neural network models don't have any aspects where you can add in ethical considerations - they're fundamentally amoral abstractions.