The scale and level of mathematical sophistication differ enormously from my own weekend project, but the underlying workflow is remarkably similar: generate many candidate approaches, eliminate most of them through exact computation and criticism, formalise what can be formalised, and reserve human judgement for novelty, correctness and significance. The important observation is not that either system solved a famous open problem. It is that mathematical experimentation itself is becoming dramatically cheaper, while proof, understanding, novelty and independent review remain scarce.
Source: Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming | Towards Data Science