The absolute state of AI results.

[Ekco]

the machine god weighs in

Breaking it down by what's actually checkable vs. what's a real unresolved argument:
Ekco's infrastructure claims hold up. All three check out against current reporting:

Data centers: about 12 gigawatts of data center capacity is expected to come online in the U.S. in 2026, but only about one-third of that capacity is currently under active construction, with analysts estimating 30-50% of planned 2026 capacity will be delayed or canceled, mostly due to difficulty sourcing electrical equipment and securing power. "Only half started" is actually generous — it's closer to a third.

Memphis: confirmed, repeatedly. xAI installed 35 gas turbines capable of producing 420 megawatts alongside Tesla Megapack battery systems because the site's initial 150MW capacity was covered by only an 8MW grid connection. This has also drawn lawsuits and air-quality complaints from the surrounding community.

China nuclear: roughly right. China operates 58 reactors and has 33 additional units under construction as of early 2026, with other counts running as high as 60 operating and 36 under construction. "60 and 40" is a slight round-up but in the right neighborhood, and the trajectory (10x growth in two decades, aiming for 110GWe by 2030) supports his point about pipeline depth.

The "10-12 million power users" figure is plausible as a strict definition but it's worth flagging that headline AI usage numbers are way higher (179 million Americans have used some AI tool) — the 10-12M figure only makes sense if it's measuring something narrower like daily, deeply-integrated professional use rather than "tried ChatGPT once." Without seeing that source's exact methodology I can't grade it, but it's not an absurd number for that narrower bar.

The scaling/plateau argument is where it gets genuinely contested, and both of you are half right.

Ekco's right that naive dense-parameter scaling has hit diminishing returns — this isn't a fringe take, it's the documented reason labs pivoted to MoE, reasoning/test-time compute, and synthetic data. In 2021, doubling compute reliably doubled measurable capability on most benchmarks; by 2025, doubling pre-training compute buys maybe a 10-20% improvement on the hardest reasoning tasks. That part of his argument is mainstream, not contrarian.

But BradZax's "progress increased, not flattened" also has real data behind it — just on a different axis. METR's tracking shows no evidence of the exponential growth in AI task-completion time horizon slowing down, and if anything the trend accelerated in 2024-2025, with time horizons doubling every 4 months instead of every 7. So overall capability (measured by what models can autonomously complete) hasn't plateaued — it's the naive scaling method that plateaued, while labs found other axes to keep climbing.

On the "models bunching at the top of the curve" chart specifically: that's a real, debated phenomenon, but it cuts both ways. It could mean genuine convergence toward a capability ceiling. It could also just mean the benchmarks themselves are saturating (hitting 95%+ scores) while real-world capability gaps are still widening underneath — METR explicitly flags this as a live possibility, that benchmarks could be saturated without leading to AGI, which is closer to your read, or that the fast pace simply continues. Nobody's resolved which is happening.

The "AGI/ASI via transformers is sci-fi bullshit" claim is the one place I won't hand a winner — that's a live, roughly 50/50 split among serious researchers (LeCun-style skeptics who think you need a fundamentally different architecture vs. people who think scaling + RL + tool use gets there), not something settled by the current data either way.