Skild's founders just dunked on the entire demo-industrial complex. Skild just disclosed a $100M annual revenue run rate, only 10 months after their first commercial deployment. 60+ paying customers, including G10 Fulfillment, Mitsui & Co, and STN Inc (data center infrastructure), with the vast majority of revenue coming from manipulation-heavy work (mobility is only 10%, Fetch solutions 4%). They argue that cherry-picked, successful demos make it feel like a use case is solved, but squeezing out the last 5% of reliability at acceptable throughput is the hard, under-appreciated part. The R&D is hard enough on its own. But real-world deployment also takes a mountain of dirty work behind the scenes: installation, integration, repairs, adapting to new processes and more. Skild plans to crack robotics RSI (recursive self-improvement). The flywheel: start with one general foundation model, deploy it into many different applications where in-context learning lets it adapt on the fly, then feed the data from all those specialized deployments back into the base model. Experience that adds a little bit to a specialist is gold to a generalist learning many tasks. Each cycle, the next deployment starts from a stronger base model and needs less specialization to get to work. The era of deployment begins.