At the NeurIPS 2025 conference, AI experts highlighted that the current strategy of scaling larger transformer models is hitting a performance ceiling, even as Google celebrated the strong results of its Gemini 3 model. Researchers argued that simply adding more data, compute, and training time no longer yields meaningful gains, pointing to a "scaling wall" and the need for new architectures such as neurosymbolic systems or world models. The consensus was that while models like Gemini 3 demonstrate impressive capabilities, they remain fundamentally limited pattern‑matchers lacking true reasoning or causal understanding, underscoring the gap to artificial general intelligence. Leer más →