A new paper demonstrates that the inherent succinctness of transformers makes basic formal verification problems computationally intractable, leading experts to advise against using them in systems requiring strict mathematical correctness. Although some practitioners note that theoretically intractable problems often have practical solutions, the general consensus is that large language models should not serve as the core of formally verified systems. Additionally, the research suggests transformers possess surprisingly low expressive power compared to recurrent neural networks, which could drive a resurgence of nonlinear RNNs for memory-efficient applications.