> The transformer architectures powering current LLMs are strictly feed-forward.

This is true in a specific contextual sense (each token that an LLM produces is from a feed-forward pass). But untrue for more than a year with reasoning models, who feed their produced tokens back as inputs, and whose tuning effectively rewards it for doing this skillfully.

Heck, it was untrue before that as well, any time an LLM responded with more than one token.

> A [March] 2025 survey by the Association for the Advancement of Artificial Intelligence (AAAI), surveying 475 AI researchers, found that 76% believe scaling up current AI approaches to achieve AGI is "unlikely" or "very unlikely" to succeed.

I dunno. This survey publication was from nearly a year ago, so the survey itself is probably more than a year old. That puts us at Sonnet 3.7. The gap between that and present day is tremendous.

I am not skilled enough to say this tactfully, but: expert opinions can be the slowest to update on the news that their specific domain may have, in hindsight, have been the wrong horse. It's the quote about it being difficult to believe something that your income requires to be false, but instead of income it can be your whole legacy or self concept. Way worse.

> My take is that research taste is going to rely heavily on the short-duration cognitive primitives that the ARC highlights but the METR metric does not capture.

I don't have an opinion on this, but I'd like to hear more about this take.

Here's a thought. Lets all arbitrarily agree AGI is here. I can't even be bothered discussing what the definition of AGI is. It's just here, accept it. Or vice versa.

Now what....? Whats happening right now that should make me care that AGI is here (or not). Whats the magic thing thats happening with AGI that wasn't happening before?

<looks out of window> <checks news websites> <checks social media...briefly> <asks wife>

Right, so, not much has changed from 1-2 years ago that I can tell. The job markets a bit shit if you're in software...is that what we get for billions of dollars spent?

I don't really understand the argument that AGI cannot be achieved just by scaling current methods. I too believe that (for any sane level of scaling anyway), but this-year's LLMs are not using entirely last-year's methods. And they, in turn, are using methods that weren't used the year before.

It seems like a prediction like "Bob won't become a formula one driver in a minivan". It's true, but not very interesting.

If Bob turned up a couple of years later in Formula one, you'd probably be right in saying that what he is driving is not a mini van. The same is true for AGI anyone who says it can't be done with current methods can point to any advancement along the way and say that's the difference.

A better way to frame it would be, is there any fundimental, quantifiable ability that is blocking AGI? I would not be surprised if the breakthrough technique has been created, but the research has not described the problem that it solves well enough for us to know that it is the breakthrough.

I realise that, for some the notion of AGI is relatively new, but some of us have been considering the matter for some time. I suspect my first essay on the topic was around 1993. It's been quite weird watching people fall into all of the same philosophical potholes that were pointed out to us at university.

Now that understanding video and projecting what happens next indicates we're getting past the LLM problem of lacking a world model. That's encouraging.

There's more than one way to do intelligence. Basic intelligence has evolved independently three times that we know of - mammals, corvids, and octopuses. All three show at least ape-level intelligence, but the species split before intelligence developed, and the brain architectures are quite different. Corvids get more done with less brain mass than mammals, and don't have a mammalian-type cortex. Octopuses have a distributed brain architecture, and have a more efficient eye design than mammals.

I used to also believe along these lines but lately I'm not so sure.

I'm honestly shocked by the latest results we're seeing with Gemini 3 Deep Think, Opus 4.6, and Codex 5.3 in math, coding, abstract reasoning, etc. Deep Think just scored 84.6% on ARC-AGI-2 (https://deepmind.google/models/gemini/)! And these benchmarks are supported by my own experimentation and testing with these models ~ specifically most recently with Opus 4.6 doing things I would have never thought possible in codebases I'm working in.

These models are demonstrating an incredible capacity for logical abstract reasoning of a level far greater than 99.9% of the world's population.

And then combine that with the latest video output we're seeing from Seedance 2.0, etc showing an incredible level of image/video understanding and generation capability.

I was previously deeply skeptical that the architecture we have would be sufficient to get us to AGI. But my belief in that has been strongly rattled lately. Honestly I think the greatest gap now is simply one of orchestration, data presentation, and work around in-context memory representations - that is, converting work done into real world into formats/representations, etc. amenable for AI to run on (text conversion, etc.) and keeping new trained/taught information in context to support continual learning.

I’d love to see one of the AI behemoths put their money where their mouth is and replace their C-suite with their SOTA chatbot.

There was a meme going around that said the fall of Rome was an unannounced anticlimactic event where one day someone went out and the bridge wasn't ever repaired.

Maybe AGI's arrival is when one day someone is given an AI to supervise instead of a new employee.

Just a user who's followed the whole mess, not a researcher. I wonder if the scaffolding and bolt-ons like reasoning will sufficiently be an asymptote to 'true AGI'. I kept reading about the limits of transformers around GPT-4 and Opus 3 time, and then those seem basic compared to today.

I gave up trying to guess when the diminishing returns will truly hit, if ever, but I do think some threshold has been passed where the frontier models are doing "white collar work as an API" and basic reasoning better than the humans in many cases, and once capital familiarizes themselves with this idea more, it's going to get interesting.

I’m under the same impression. I don’t think LLMs are the path to AGI. The “intelligence” we see is mostly illusory. It’s statistical repetition of the mediocre minds who wrote content online.

The intelligence we think we recognize is simply an electronic parrot finding the right words in its model to make itself useful.

State of the Art Large Language Models are already Generally Intelligent, in so far as the term has any useful meaning. Their biggest weakness are long horizon planning competency, and spatial reasoning and navigation, both of which continue to improve steadily and are leaps and bounds above where they were a few years ago. I don't think there's any magic wall. Eventually they will simply get good enough, just like everything else.

I think that AGI has already happened, but it's not well understood, nor well distributed yet.

OpenClaw, et al, are one thing that got me nudged a little bit, but it was Sammy Jankis[1,2] that pushed me over the edge, with force. It's janky as all get out, but it'll learn to build it's own memory system on top of an LLM which definitely forgets.

[1] https://sammyjankis.com/

[2] https://news.ycombinator.com/item?id=47018100

How will we know if its AGI/Not AGI? (I don't think a simple app is gonna cut it here haha)

What is the benchmark now that the Turing test has been blown out of the water?

Our brains evolved to hunt prey, find mates, and avoid becoming hunted ourselves. Those three tasks were the main factors for the vast majority of evolutionary history.

We didn't evolve our brains to do math, write code, write letters in the right registers to government institutions, or get an intuition on how to fold proteins. For us, these are hard tasks.

That's why you get AI competing at IMO level but unable to clean toilets or drive cars in all of the settings that humans do.

Until I can get a robot wife maid im not worried about or even confident I will ever see actual AGI. People have been predicting it for as long as fusion power and while progress has been made, we might still be like Romans dreaming of flight.

As far as I'm concerned, it's already here.

I think it's really poor argument that AGI won't happen because model doesn't understand physical world. That can be trained the same way everything else is.

I think the biggest issue we currently have is with proper memory. But even that is because it's not feasible to post-train an individual model on its experiences at scale. It's not a fundamental architectural limitation.

AGI is a messy term, so to be concise, we have the models that can do work. What we lack is orchestration, management, and workflows to use models effectively. Give it 5 years and those will be built and they could be built using the models we have today (Opus 4.6 at the time of this message).

I'm certainly not holding my breath.

In a handful of prompts I got the paid version of ChatGPT to say it's possible for dogs to lay eggs under the right circumstances.

If AGI can be defined as meeting the general intelligence of a Redditor, we hit ASI a while ago. Highly relevant comment <https://www.reddit.com/r/singularity/comments/1jh9c90/why_do...> by /u/Pyros-SD-Models:

>Imagine you had a frozen [large language] model that is a 1:1 copy of the average person, let’s say, an average Redditor. Literally nobody would use that model because it can’t do anything. It can’t code, can’t do math, isn’t particularly creative at writing stories. It generalizes when it’s wrong and has biases that not even fine-tuning with facts can eliminate. And it hallucinates like crazy often stating opinions as facts, or thinking it is correct when it isn't.

>The only things it can do are basic tasks nobody needs a model for, because everyone can already do them. If you are lucky you get one that is pretty good in a singular narrow task. But that's the best it can get.

>and somehow this model won't shut up and tell everyone how smart and special it is also it claims consciousness. ridiculous.

I've said it before and I'll say it again, all AI discussion feels like a waste of effort.

“yes it will”, “no it won’t” - nobody really knows, it's just a bunch of extremely opinionated people rehashing the same tired arguments across 800 comments per thread.

There’s no point in talking about it anymore, just wait to see how it all turns out.

AGI is here. 90%+ of white collar work _can_ be done by an LLM. We are simply missing a tested orchestration layer. Speaking broadly about knowledge work here, there is almost nothing that a human is better at than Opus 4.6. Especially if you're a typical office worker whose job is done primarily on a computer, if that's all AGI is, then yeah, it's here.