The arXiv preprint from Zheng et al. reports micro-transfer printing of thin-film lithium niobate onto 200 mm silicon photonics wafers across four full wafers, achieving >95% yield and 420 nm 3-sigma placement accuracy. This experimental demonstration on full wafers, rather than die-level bonding, directly addresses the AI-driven demand for low-energy, high-bandwidth interconnects. Unlike earlier heterogeneous integration efforts that relied on direct bonding with limited throughput, this MTP approach enables parallel transfer of hundreds of devices per print cycle. The reported metrics—insertion loss below 2 dB across 600 phase modulators and >70 GHz bandwidth on tested subsets with 4 V half-wave voltage—outperform many prior lab-scale TFLN-on-SiPho results. However, the preprint nature means no peer review has yet validated long-term reliability under data-center thermal cycling. The work overlooks direct comparisons to commercial foundry bonding yields at GlobalFoundries or TSMC, where placement accuracy often exceeds 200 nm but at higher cost per wafer. Two related sources highlight the pattern: a 2023 Nature Photonics review on photonic AI accelerators notes that modulator bandwidth above 60 GHz remains the bottleneck for optical I/O scaling, while a 2024 IEEE JSTQE paper on MTP for InP lasers documents similar 90%+ yields but warns of defect propagation at 300 mm scales. This method could accelerate replacement of copper interconnects in hyperscale clusters, shifting manufacturing jobs from traditional CMOS fabs toward specialized photonics assembly lines in Europe and Asia. Limitations include the subset-only high-speed testing and absence of full link-level BER measurements, leaving open questions on crosstalk in dense arrays.