I get the sentiment, but I think declaring scaling "dead" is premature and misses the point.

First, let's be honest about GPT-5. The article cherry-picks the failures. For 95% of my workflow -- generating complex standalone code, summarizing and finding issues in new code, drafting technical documentation, summarizing dense research papers -- it's a massive step up from GPT-4. The "AGI" narrative was always a VC-fueled fantasy. The real story is the continued, compounding utility as a tool. A calculator can't write a poem, but it was still revolutionary.

Second, "scaling" isn't just compute * data. It's also algorithmic improvements. Reasoning was a huge step forward. Maybe the next leap isn't just a 100x parameter increase, but a fundamental architectural shift we haven't discovered yet, which will then unlock the next phase of scaling. Think of it like the transition from single-core to multi-core CPUs. We hit a frequency wall, so we went parallel. We're hitting a density wall with LLMs, the next move is likely towards smarter, more efficient architectures.

The fever dream isn't superintelligence. The fever dream was thinking we'd get there on a single, straight-line trajectory with one single architecture. The progress is still happening, it's just getting harder and requires more ingenuity, which is how all mature engineering fields work.