As AI systems grow more capable, the field of AI safety has shifted from theoretical concern to urgent priority. In 2025, we saw major labs adopt more rigorous evaluation frameworks, with red-teaming becoming standard practice before model releases. Governments began drafting meaningful legislation, and the EU AI Act set precedents that other jurisdictions are now studying closely. The conversation has matured: rather than debating whether safety matters, researchers are now focused on how to measure it, how to enforce it, and how to balance caution with the genuine benefits these systems can provide.

Looking toward 2026, I expect alignment research to receive significantly more funding and attention. We'll likely see the emergence of industry-wide safety standards, perhaps coordinated through bodies similar to how aviation regulates itself. Interpretability — understanding what models are actually doing internally — will move from academic curiosity to practical necessity as regulators demand explanations for high-stakes decisions. The challenge will be ensuring that safety measures keep pace with capability gains, rather than trailing behind as they have historically. The organisations that treat safety as a competitive advantage rather than a compliance burden will likely define the trajectory of the field.