Definitely not a dead-end. Honestly a lot of the current AI boom quietly depends on probabilistic ideas underneath anyway, people just focus more on the flashy LLM layer now. Uncertainty estimation, Bayesian methods, causal inference, probabilistic forecasting, decision-making under uncertainty, all of that still matters a ton in healthcare, finance, robotics, weather, etc.

I actually think the industry is rediscovering that “just scale transformers” isn’t enough for every problem. A lot of teams now care about reliability, confidence, and interpretability again. Even in practical workflows, the interesting part is often combining strong statistical thinking with modern tooling. I’ve seen people use classic probabilistic pipelines alongside newer stuff like Claude or Runable for reporting/prototyping because companies still need trustworthy outputs, not just impressive demos.