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Hardness of approximation (HA) -- the phenomenon that, assuming P $\neq$ NP, one can easily compute an $ε$-approximation to the solution of a discrete computational problem for $ε> ε_0 > 0$, but for $ε< ε_0$ it suddenly becomes intractable -- is a core phenomenon in the foundations of computations that has transformed computer science. In this paper we study the newly discovered phenomenon in the foundations of computational mathematics: generalised hardness of approximation (GHA) -- which in spirit is close to classical HA in computer science. However, GHA is typically independent of the P vs. NP question in many cases. Thus, it requires a new mathematical framework that we initiate in this paper. We demonstrate the hitherto undiscovered phenomenon that GHA happens when using AI techniques in order to train optimal neural networks (NNs). In particular, for any non-zero underdetermined linear problem the following phase transition may occur: One can prove the existence of optimal NNs for solving the problem but they can only be computed to a certain accuracy $ε_0 > 0$. Below the approximation threshold $ε_0$ -- not only does it become intractable to compute the NN -- it becomes impossible regardless of computing power, and no randomised algorithm can solve the problem with probability better than 1/2. In other cases, despite the existence of a stable optimal NN, any attempts of computing it below the approximation threshold $ε_0$ will yield an unstable NN. Our results use and extend the current mathematical framework of the Solvability Complexity Index (SCI) hierarchy and facilitate a program for detecting the GHA phenomenon throughout computational mathematics and AI.