Biomimetic olfactory and taste biosensors replicate human sensory functions by coupling selective biological recognition elements (such as receptors, binding proteins, or synthetic mimics) with highly sensitive transducers (including electrochemical, transistor, optical, and mechanical types). This review summarizes recent progress in olfactory and taste biosensors focusing on three key areas: (i) materials and device design, (ii) artificial intelligence (AI) and data fusion for real-time decision-making, and (iii) pathways for practical application, including hybrid platforms, Internet of Things (IoT) connectivity, and regulatory considerations. We provide a comparative analysis of smell and taste sensing methods, emphasizing cases where integrating both modalities enhances sensitivity, selectivity, detection limits, and reliability in complex environments like food, environmental monitoring, healthcare, and security. Ongoing challenges are addressed with emerging solutions such as antifouling/self-healing interfaces, modular cartridges, machine learning (ML)-assisted calibration, and manufacturing-friendly approaches using scalable microfabrication and sustainable materials. The review concludes with a practical roadmap advocating for the joint development of receptors, materials, and algorithms; establishment of open standards for long-term stability; implementation of explainable/edge AI with privacy-focused analytics; and proactive collaboration with regulatory bodies. Collectively, these strategies aim to advance biomimetic smell and taste biosensors from experimental prototypes to dependable, commercially viable tools for continuous chemical sensing in real-world applications.

The idea of the “alignment tax” often appears in discussions about developing artificial intelligence. It suggests that safety measures slow down innovation and competitiveness. This opinion piece challenges that view. It claims that safety should be seen as an important part of technological capability, not as an added cost. By looking at examples from the aviation industry and recent progress in AI research, the article shows how interpretability, constitutional AI, and scalable oversight lead to more reliable, controllable, and socially acceptable systems. It argues that the real cost comes not from investing in safety, but from ignoring it. This neglect can cause societal harm, erode public trust, and invite more regulatory scrutiny. By viewing safety as a driver of long-term innovation, this article encourages the integration of alignment research into the foundation of AI development. This approach aims for sustainable and responsible progress.