Researchers at the University of California San Diego have published a comprehensive review in the Journal of Applied Microbiology outlining how artificial intelligence and metabolic modeling can transform microbial biocontrol into a reliable defense against antimicrobial resistance in built environments. Led by Dr. Kathleen Furtado alongside Dr. Jack Gilbert and Dr. Maxwell Neal, the study addresses the escalating global health crisis of AMR, which is projected to cause up to ten million deaths annually by 2050. Current reliance on chemical disinfectants has proven inadequate, as they often fail to eliminate resilient pathogens and may inadvertently accelerate resistance development. The review examines microbial biocontrol as a complementary strategy, utilizing beneficial bacteria such as Bacillus subtilis to outcompete, inhibit, or physically displace disease-causing organisms. While this approach has shown promise in agricultural and clinical settings, its application in hospitals, schools, and residential buildings has yielded inconsistent results. These discrepancies stem from unpredictable environmental stressors, nutrient fluctuations, complex microbial ecosystems, and a fragmented regulatory framework for risk assessment. To resolve these challenges, the researchers propose an integrated AI-driven methodology. By synthesizing metabolic models, multi-omic datasets, and targeted laboratory experiments, artificial intelligence can predict how engineered biocontrol strains will interact with established microbial communities and target pathogens. This predictive capacity enables the identification of potential biosafety risks, such as the horizontal transfer of resistance genes, and facilitates the selection of optimal strains for specific environments. The proposed framework operates within iterative design-test-learn cycles, where AI-generated hypotheses guide new experiments, which in turn refine the underlying models. The study highlights two primary deployment strategies. The first involves optimized biocontrol formulations for spray cleaners, selected through modeling to maximize survival and antimicrobial activity on built surfaces. The second centers on engineered living materials, where beneficial microbes are embedded or printed into construction mediums like concrete, ceramics, and cellulose. Encapsulating these organisms within structural matrices prevents environmental escape while maintaining competitive inhibition through nutrient competition and targeted metabolite secretion. Metabolic modeling further assists in identifying base materials that preserve structural integrity without compromising microbial viability. Dr. Furtado emphasizes that computational predictions must be rigorously validated through mechanistic experimentation to confirm real-world efficacy and safety. Current knowledge regarding how biocontrol microbes express competitive traits in dynamic built environments remains limited. AI-guided modeling is positioned to prioritize and design these critical experiments, accelerating the development of safe, context-specific biocontrol solutions. As chemical disinfection methods face mounting limitations, the convergence of synthetic biology, metabolic engineering, and artificial intelligence offers a scalable pathway to mitigate antimicrobial resistance in everyday spaces.