The absolute state of AI results.

[Ekco]

Only half the planned data centers have started this year and our energy infrastructure is dogshit, Elon has mobile gas turbines running in the parking lot of the colossus data center because the Memphis grid can't supply it, we're currently compute restrained with only 10 million ai power users in the country

When evaluating the market through this strict lens, the estimate of 10 to 12 million true, deeply integrated AI power users in the United States is incredibly accurate.

China has 60 nuclear reactors with 40 under construction with a population pool of non retards orders of magnitude larger to pull from, if AGI is brute forceable by just scaling they've already won

Lucky for us AGI and certainly ASI by any of the like 100+ definitions out there is some sci-fi bullshit that transformers doing linear algebra matrix multiplications isn't going to achieve

[BradZax]

Originally Posted by Ekco [You must be logged in to view images. Log in or Register.]

Lucky for us AGI and certainly ASI by any of the like 100+ definitions out there is some sci-fi bullshit that transformers doing linear algebra matrix multiplications isn't going to achieve

not according to that google doc.

I also like in that google doc that it debunks the myth that ai progress has flattened and it shows that actually, it has increased.

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[Ekco]

It's already no longer exponential and plateauing like moore's law with CPUs and other hardware, that's literally what your graph shows of them all bunchin up together at the top of the curve there, 4.6, 4.7 fable/mythos chatgpt 5.5 the new China one are all still on the same tier the difference between their capabilities and the previous ones are a fraction compared to the previous ones and another tier down

They've already shifted strategy from one giant parameter model to a bunch of smaller MOE mixture of expert models in a trench coat because they literally did hit a ceiling with the previous model training method

[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.

[Duik]

Now that the (largely) freebie tokens will dry up because billionaires want they money back those of you that ask A.I agents stupid questions like Summarize the posts of forum user <insert user> and give me bullet points as to why they are a boomer for free will defo not wanna pay for that shit.

A.I agents need to make money for their digital pimps.
Start paying.
Medical imaging manipulation/interogation? Good usecase.
Vibe coding with guided prompts by knowledgeable programers? As much as i hate to admit it. Good usecase.

Putting a suit and tie on ya cat photo? Go for it, but be prepared to pay for it with little to no returns.

Welcome to A.I.

[BradZax]

Originally Posted by Ekco [You must be logged in to view images. Log in or Register.]

It's already no longer exponential and plateauing like moore's law with CPUs and other hardware, that's literally what your graph shows of them all bunchin up together at the top of the curve there, 4.6, 4.7 fable/mythos chatgpt 5.5 the new China one are all still on the same tier the difference between their capabilities and the previous ones are a fraction compared to the previous ones and another tier down

They've already shifted strategy from one giant parameter model to a bunch of smaller MOE mixture of expert models in a trench coat because they literally did hit a ceiling with the previous model training method

they are not bunching up, what you're seeing is there are more companies developing more AI, the progress over time is doing exponential growth.

Power consumption aside, that curve will solve that problem on its own when it needs to.

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[Ekco]

that graph now that ive actually look at it is just about time on task and stops at 16 hours dues to unreliable test suite, whatever the fuck that means, write a better test, either way its outdated as fuck Claude runs for days now

Claude can run continuously for days on massive coding projects thanks to dynamic, agentic harnesses like the Claude Agent SDK or Claude Code

so whoever put that chart together doesn't know how people have been using the models for numerous months now, in parallel with a overseer agent and dozens of sub agents and all that bullshit in an agentic self-correction loop

the thing that actually matters is capability and the plateau is way more pronounced in those charts in the models released in the last year, the big gains are in reducing the time to complete a task successfully, some giant codebase can take chatgpt 5.5 3 days to work on and Mythos supposedly did the work correctly in like 10 hours or something

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we've hit that ceiling, the is AGI even possible easily by just making a 1 trillion parameter model type idea and the answer is no and charts like this are pointless now because of diminishing returns of just training larger and larger parameter single models Qwen opensource is at like 350b parameters but that's probably just adding up all the separate MoE models

While frontier closed-source providers (Anthropic, OpenAI, Google) no longer disclose exact parameter counts for models like Claude Fable 5, Claude Opus 4.8, or GPT-5.5 Pro, the landscape for open-weights and verifiable models has scaled significantly heading into mid-2026.

Frontier Open MoE Qwen3 235B A22B / Qwen3.5-397B-A17B 235B – 397B total (17B–22B active per token)

yeah, they stopped reporting the parameters because number no longer going up = scary for investors interested in two companies about to IPO

Major Paradigm Shifts Since Late 2024:

Active vs. Total Parameters (MoE Dominance): Large open-weights models have shifted aggressively toward Mixture-of-Experts (MoE) architectures (seen in the Qwen3 and DeepSeek-V4 series). A model may have up to 397B total parameters sitting in storage, but only routes ~17B to 22B active parameters per token, drastically reducing inference latency while preserving massive knowledge depth.

The "Medium" Sweet Spot Shift: The traditional 7B baseline has moved up. Architectures like Gemma 3 (12B) and Qwen3.5 (9B) maximize dense compute efficiency, effectively rendering the old 3B–7B performance tier obsolete for complex multistep coding or agentic loops.

so the sweet spot, is the same model im using for Kaia on a GPU from 2021 that costs like 250 bucks, a model running LOCALLY on your cell phone has enough juice for 99.9% of user queries if built right to use tools like let_me_fucking_google_that_for_you.py considering what most people are actually uses these chatbots for

so not only is AGI/ASI not going to happen, all these companies are going to go bankrupt causing a deep recession because their business plan we started this journey with doesn't make any sense anymore

open source models wins on both ends of the spectrum, locally run open source wins for a non trivial chunk of the consumer/enthusiast market and enterprise coding just got something that costs 1/5th of a Claude or ChatGPT seat dropped in their lap thanks to China

[Ekco]

[BradZax]

Originally Posted by Ekco [You must be logged in to view images. Log in or Register.]

so whoever put that chart together doesn't know how people have been using the models

The people that put the chart together are the ones that made the models that are throttled 99% those people have been using.

[Ekco]

It actually isn't, I went and scrolled the paper lol