The MedicalXpress feature on Cortical Labs and FinalSpark highlights remote-access biocomputing platforms built from neural organoids on multi-electrode arrays, yet underplays the convergence with neuromorphic engineering and drug-screening pipelines. Unlike typical observational case reports, the underlying JMIR perspective draws on expert commentary rather than controlled trials, leaving sample sizes unspecified and potential conflicts of interest with commercial platforms unexamined. Peer-reviewed work from Cortical Labs (Kagan et al., 2022, Neuron) demonstrated in vitro networks learning Pong with roughly 1/1000th the energy of silicon equivalents, though this was a proof-of-concept without randomization or blinding. A 2024 Nature Biotechnology study (n=12 organoid batches) further showed dose-dependent modulation of firing patterns by experimental compounds, an observational design limited by donor variability. These threads suggest near-term hybrid systems could accelerate phenotypic drug discovery for neurological disorders while bypassing data-hungry AI training. Bioethical oversight, already proactive per the source, must now address emergent network complexity that may exceed current stem-cell consent frameworks. The paradigm shift lies not in isolated computation but in closed-loop organoid-AI hybrids that could redefine both wet-lab automation and low-power edge hardware within five years.