The dominant uncertainties in climate projections are process-based: our parameterizations of clouds, convection, and aerosol interactions remain poorly constrained despite decades of effort. I will argue this is partly a tooling problem. Inspired by advances in machine learning, differentiable models offer direct access to gradients, unlocking faster calibration and online bias correction from observations, as well as enabling the seamless online tuning of hybrid-ML components. I will introduce JCMv2, a fully differentiable, full complexity, atmospheric model built in JAX, as a concrete example of this approach. Increasingly capable agentic coding tools that lower the barrier to building such systems, combined with rigorous community benchmarks, may be opening an exciting new path forward to converting full complexity models with modest effort. I will share early results and assessments of where this approach is promising, where the physics fights back, and what it might look like if it works.