New artificial intelligence (AI) offerings, such as Open AI's ChatGPT, Google's Gemini, and Anthropic's Claude, have fascinated business leaders and the public alike. While the technical progress that has garnered headlines is impressive, the economic feasibility of these AI systems can fall short. For firms to justify adopting new AI capabilities, these systems must create value that exceeds their cost. However, because the upfront development costs are enormous, just achieving breakeven on AI investments will be a challenge unless firms have the deployment scope necessary to sufficiently amortize costs. Put another way, for AI to move from a few generalist systems to the myriad of specialized systems needed for deployment throughout the economy, an enormous amount of costly 'last mile' customization will be needed. Whether such customization can be justified economically will depend on the performance needs of the companies deploying them and on the ability of technology providers to achieve greater scale.