Nobel laureate and Google DeepMind co-founder Hassabis proposed an "Einstein test": whether AI, given only knowledge available in 1911, could formulate relativity by 1915, reasoning beyond its training material as a strong test of AGI. Nature gave this test its name. In March of this year, independent researcher Michael Hla pushed the knowledge cutoff date back to 1900 and trained a historical language model, GPT-1900, from scratch, aiming to enable it to rediscover the photon, special relativity, and general relativity. GPT-1900 generated a passage remarkably similar to Einstein's 1905 paper: "Light may not be continuous, but rather composed of many distinct parts with different frequencies." But the experiment ultimately did not produce an "AI Einstein," because GPT-1900 failed on most physical tasks, its impressive responses heavily relied on human-curated questions and prompts, and even the supposed isolation within a 1900 knowledge world did not fully shield it from the influence of modern AI. Nature's latest study reviews these attempts and points to a more challenging question than "Did AI get the answer right?": Can AI truly generate genuinely new ideas? And what is still missing for it to become a scientist? Hla named his project Machina Mirabilis, inspired by Einstein's "Annus Mirabilis." In 1905, 26-year-old Einstein published four groundbreaking papers. Hla trained a 3.3-billion-parameter