Optimization has been a thread running through the history of printing, from the motivation of Johannes Gutenberg developing moveable type to the present efforts to apply AI techniques to solving a broad range of printing challenges. The need for optimization here ranges from the solution of specific, well-defined challenges like the prediction of print color from as few measurements as possible, via the boosting of ink efficiency and printed image quality to the need for ease of use and automation necessitated by a desire to minimize human intervention in print manufacturing. While the former category of challenges is well suited to various flavors of machine learning, the latter becomes approachable thanks to the advent of large language models and other generative AI methods. A critical factor in this optimization spectrum is also the control domain in which printed output can be specified and therefore also adjusted. Here the HANS paradigm offers a clear benefit by making the space in which print is controlled linear, which allows for optimization processes performed on top of it to be directed at the specific challenges at hand, instead of also having to compensate for the ill-behavedness of conventional print control spaces. The paper concludes with a bit of blue-sky thinking about what the near future of print optimization may look like, where AI moves from particular, narrow application to providing an end-to-end infrastructure.