We keep asking "how close are we to AGI?" — but what if that's the wrong question entirely?
I've spent the past few months going through cognitive science, neuroscience, ML architecture, philosophy of mind, and computer architecture trying to understand one thing: are we actually building intelligence, or something else?
My conclusion: the industry has mislabeled what it's building. Current LLMs are not artificial intelligence in any structural sense. They're artificial rote learners — frozen pattern matchers that cannot update a single weight from lived experience.
The post builds a scale from 0.0 to 3.0 based purely on what a system structurally is, not what it produces:
0.0 — deterministic computing
0.2 — where every LLM, diffusion model, and Mamba architecture sits today
0.4 — neuromorphic hardware (the physical prerequisite nobody talks about)
1.0 — what would actually deserve the name "AI"
3.0 — ASI in any meaningful architectural sense
The gap between 0.2 and 1.0 is not a scaling problem. It's an unsolved architectural revolution.
I also cover three empirical proofs the current paradigm has hit its ceiling, the Von Neumann memory wall, and why an elephant modifying a branch to solve a self-identified problem is architecturally more significant than any current LLM.
Full blog is here: https://sanidhyakulkarni.blogspot.com/2026/07/IntelligenceHorizon.html
Genuinely interested in pushback from people who think the scale is wrong or the architectural argument doesn't hold.share if you like it .