Don't believe the hype, this is tribal thinking. Everybody seems to have these widely diverging opinions on AI lately. What does AGI have to do with predicting the next token stochastically like a parrot? Oh, people say you can brute-force AGI, if only things are answered correctly. I get that, I still see SOTA models sometimes fail like babies. I also mostly see them perform at a much higher intelligence and work ethics than I can, but maybe I'm too hard on myself.

Anyway, here's something I've recently build that shows the HN consensus when it comes to AI-Coding (spoiler: they say it's quite good): https://is-ai-good-yet.com/ Is AI “good” yet? – A survey website that analyzes Hacker News sentiment toward AI coding.

No serious person thinks LLMs will be the method to create AGI. Even Sam Altman gave that up.

Anyone still saying they'll reach AGI is pumping a stock price.

Separately and unrelated, companies and researchers are still attempting to reach AGI by replacing or augmenting LLMs with other modes of machine learning.

Probably the biggest thing that serious predictions are relying on is the METR graph:

https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...

It shows a remarkably consistent curve for AI completing increasingly difficult coding tasks over time. In fact, the curve is exponential, where the X axis is time and the Y axis is task difficulty as measured by how long a human would take to perform the task. The current value for 80% success rate is only 45 minutes, but if it continues to follow the exponential curve, it will only take 3 years and change to get to a full 40 hour human work week's worth of work. The 50% success rate graph is also interesting, as it's similarly exponential and is currently at 6 hours.

Of course, progress could fall off as LLMs hit various scaling limits or as the nature of the difficulty changes. But I for one predicted that progress would fall off before, and was wrong. (And there is nothing saying that progress can't speed up.)

On the other hand, I do find it a little suspicious that so many eggs are in the one basket of METR, prediction-wise.

> People making random claims about AGI soon is really weakening my confidence in AI in general

See it as a simple BS detector.

Look carefully at the 'why' in the person / influencer and you almost answered your own question.

> I don't know if the investments in AI are worth it but am I blind for not seeing any hope for AGI any time soon.

> People making random claims about AGI soon is really weakening my confidence in AI in general.

The "people" that are screaming the loudest and making claims about AGI are the ones that have already invested lots of money into hundreds of so-called AI companies and then create false promises about AGI timelines.

Deepmind was the one that took AGI seriously first which it actually meant something until it became meaningless, when every single AI company after OpenAI raised billions in funding rounds over it.

No one can agree as to what "AGI" really means, It varies depending who you ask. But if you look at the actions made by these companies invested in AI, you can figure out what the true definition converges to, with some hints [0].

But it is completely different to what you think it is, and what they say it is.

[0] https://news.ycombinator.com/item?id=46668248

LLMs are real, far from what we could call AI, but AI is here because corporate marketing said so.

People talk about AGI because this is how corporate marketing will create AGI, even if AGI is not near to what we could call possible.

But this is how things work now, corporate marketing says what is real and what is not.

While large language models don't have enough nuance for AGI, there is some promise still in multi-modal models, or models based purely on other high-bandwidth data like video. So probabilistic token-based models aren't entirely out of the running yet.

Part of the problem with LLMs in particular is ambiguity -- this is poisonous to a language model. And English in particular is full of it. So another potential that is being explored is translating everything (with proper nuance) to another language that is more precise, or by rewriting training data to eliminate any ambiguities by using more exact English.

So there are ideas and people are still at it. After all, it usually takes decades to fully exploit any new technology. I don't expect that to be any different with models.

Pretty much as soon as the idea of computers were invented people have been dreaming about AGI. There's no practical usecase, but it's what people want, so there will always be hype and people trying to get there.

It's a very nrw technology which progresses very fast and no ceiling is in sight.

Right now it seems like this could lead to AGI very fast (5-15 years).

Especially because the richest companies in the world are investing extrem amounts of money and are very fast in pivoting into it.

Image and video gen alone made crazy progress.

We have capabilities today which were unthinkable just a few years back

Does it matter if AGI ever come any time soon? Does the current AI ( LLM ) provide or generate any value? If not we can agree to disagree.

Is it perfect? No. Far from it. Is it useful in some, and in the future many situations, Yes.

I am a believer of agentic LLM and aside from a few downsides, it has been imensely useful for me.

Having said that, I could not care less about AGI and don't see how it's any relevant to what I wanna do with AI.

I guess people talk about AGI as in human abilities or better AI because it'll have a big effect when it gets here.

I think a lot of confusion with skeptics is they think - oh someone's invented the LLM algorithm but it's not that good - what's the big deal?

The people who think it's coming eg. Musk, Altman, Kurzweil, the Wait Not Why guy and myself tend to think of it more coming down to hardware - the brain's a biological computer and as computer hardware get's faster each year it'll overtake at some stage. The current backprop algo was invented around 1982. It works now because hardware.

Also the present algorithms are a bit lacking but now we have the hardware to run better algorithms, billions of dollars and many of the best minds are getting thrown at that. Before the hardware was there there wasn't much financial motivation to do so. So I think things will advance quite quickly there.

(Wait But Why thing from eleven years ago. Has cartoons. Predicted human level about 2025 https://waitbutwhy.com/2015/01/artificial-intelligence-revol...

Moravec's 1997 paper "When will computer hardware match the human brain?" Quite science based - predicted "required hardware will be available in cheap machines in the 2020s" https://jetpress.org/volume1/moravec.pdf

And here we are.)

I think modern agentic tools let you take bigger steps when programming. They’re still fallible and you need to be mentally engaged when using them. But they’re a programmer’s power drill.

I think what we have is mostly AGI. It’s artificial , it’s intelligence, and most important it’s general. It may never get an IQ about 75 or so, but it’s here.

hello,

idk ... even sam altman talked a lot about AGI *) recently ...

*) ads generated income

*bruhahaha* ... ;^)

just my 0.02€

Ten years ago I believed we'll have AGI/end-of-the-world/Singularity circa 2040, and meanwhile in the 2020s we will chill out in a futuristic, booming world of un-smart innovations like 3D printing, VR and the Metaverse.

Then, in March 2023, with GPT-4, I said that we'll have AGI only ten years later, and the progress in the last few years (multimodal stuff, reasoning, coding agents) hasn't changed this view.

AGI is already here and arrived without a bang, AGI arrived last year. To each his own and their own reality.

because it’s all about the adjusted gross income

I'm pretty sure how AGI seems to be defined by your typical HN commenter (if it they've managed to define it at all) is very different to how the AI firms define AGI.

As far as I can tell, HN defines an AGI as something that can do all the things a human can do better than a human. Or to put it another way if there is something the AGI can't do better than a human expert, then it will be loudly pointed to as evidence we haven't developed a true AGI yet.

Meanwhile I'm pretty sure the AI firms are using a very simple definition of AGI to justify their stock price: an AGI is an AI that can create other AI's faster / more cheaply than their own engineers can. Once that barrier is broken you task the AGI with building a better version it itself. Rinse, lather and repeat a few times, and they dominate the market with the best AI's. Repeat many more times and the universe becomes paperclips.