Solid line of reasoning on hallucination as an epistemic boundary marker rather than a “bug,” that distinction gets missed a lot. Two things worth pushing back on though.
On the energy argument specifically, the more precise framing right now isn’t “we’re running out of power,” it’s that grid connectivity and interconnect/permitting timelines are the actual binding constraint, connecting a new data center can take 4-10 years while the facility itself gets built in 2-3, that’s a delivery/logistics bottleneck, not a hard physical ceiling on available energy. Recent analysis (International AI Safety Report 2026) actually concludes energy bottlenecks likely won’t prevent compute scaling through the end of the decade, since 10GW-scale buildouts are already underway in regions being chosen specifically for power surplus, the constraint is shifting geography and slowing pace, not stopping it. So “AI race decided by energy and materials before intelligence” is arguable, but the more accurate 2026 story is a grid/permitting bottleneck that reroutes and delays investment rather than a wall.
On the philosophy side, “genuine intelligence needs to be self-aware” is doing a lot of unexamined work, that’s one contested definition among several in the AGI literature (capability-based, generality-based, economic-impact-based definitions all exist and don’t require self-awareness as a precondition), so the argument assumes its conclusion a bit. Otherwise the “broad but bounded, not general” framing and the call for smaller/specialized/efficient models over chasing a singularity is a fair take and matches where a lot of the actual research community has already landed.