At Applied Compute, we believe Training wins Inference. In the limit, inference alone is a commodity: providers serve the same models, compete on price, and race toward thinner margins. Training changes that. Once an AI workload reaches scale, serving a generic model stops making sense. Companies will fine-tune, distill, and continually optimize models around their own data, product, economics, and definition of quality. That is the real advantage of open-weight models: you have the weights. You can make a model better on your evals, faster for your workload, and cheaper per token. Over time, companies will choose the best model for their specific task - not the best generic model. We believe that, in the long run, more than 90% of inference on open-weight models will come from trained variants rather than untouched base models. Training also de-commoditizes inference! A provider serving the same off-the-shelf model as everyone else has little room to differentiate. But a company will pay more for a trained version of GLM-5.2 that materially outperforms the base model on the evals that they care about. The providers that help companies train differentiated models (Applied Compute) will be best positioned to serve them in production, too. Training and inference will converge into one continuous system. The long-term end state is continual learning: models that improve from proprietary data, real-world usage, and direct feedback. The companies that close this loop fastest will build products that get better with every interaction and compound their lead over time.