> I can definitely believe that in 2026 someone at their computer with access to money can send the right emails and make the right bank transfers to get real people to grow corn for you.

I think this is the new turing test. Once it's been passed we will have AGI and all the Sam Altmans of the world will be proven correct. (This isn't a perfect test obviously, but neither was the turing test)

If it fails to pass we will still have what jdthedisciple pointed out

> a non-farmer, is doing professional farmer's work all on his own without prior experience

I am actually curious how many people really believe AGI will happen. Theres alot of talk about it, but when can I ask claude code to build me a browser from scratch and I get a browser from scratch. Or when can I ask claude code to grow corn and claude code grows corn. Never? In 2027? In 2035? In the year 3000?

HN seems rife with strong opinions on this, but does anybody really know?

▲ cevn an hour ago | parent | next [-]

I think once we get off LLM's and find something that more closely maps to how humans think, which is still not known afaik. So either never or once the brain is figured out.

	▲	autoexec 17 minutes ago \| parent [-]
		I'd agree that LLMs are a dead end to AGI, but I don't think that AI needs to mirror our own brains very closely to work. It'd be really helpful to know how our brains work if we wanted to replicate them, but it's possible that we could find a solution that is entirely different from human brains while still having the ability to truly think/learn for itself.

▲ bayindirh an hour ago | parent | prev [-]

Researchers love to reduce everything into formulae, and believe that when they have the right set of formulae, they can simulate something as-is.

Hint: It doesn't work that way.

Another hint: I'm a researcher.

Yes, we have found a great way to compress and remix the information we scrape from the internet, and even with some randomness, looks like we can emit the right set of tokens which makes sense, or search the internet the right way and emit these search results, but AGI is more than that.

There's so much tacit knowledge and implicit computation coming from experience, emotions, sensory inputs and from our own internal noise. AI models doesn't work on those. LLMs consume language and emit language. The information embedded in these languages are available to them, but most of the tacit knowledge is just an empty shell of the thing we try to define with the limited set of words.

It's the same with anything we're trying to replace humans in real world, in daily tasks (self-driving, compliance check, analysis, etc.).

AI is missing the magic grains we can't put out as words or numbers or anything else. The magic smoke, if you pardon the term. This is why no amount of documentation can replace a knowledgeable human.

...or this is why McLaren Technology Center's aim of "being successful without depending on any specific human by documenting everything everyone knows" is an impossible goal.

Because like it or not, intuition is real, and AI lacks it. Irrelevant of how we derive or build that intuition.

	▲	smaudet 33 minutes ago \| parent \| next [-]
		> There's so much tacit knowledge and implicit computation coming from experience, emotions, sensory inputs and from our own internal noise. The premise of the article is stupid, though...yes, they aren't us. A human might grow corn, or decide it should be grown. But the AI doesn't need corn, it won't grown corn, and it doesn't need any of the other things. This is why, they are not useful to us. Put it in science fiction terms. You can create a monster, and it can have super powers, _but that does not make it useful to us_. The extremely hungry monster will eat everything it sees, but it won't make anyone's life better.
	▲	godelski 8 minutes ago \| parent \| prev [-]
		`> Hint: It doesn't work that way.` I mean... technically it would work this way but, and this is a big but, reality is extremely complicated and a model that can actually be a reliable formula has to be extremely complicated. There's almost certainly no globally optimal solutions to these types of problems, not to mention that the solution space is constantly changing as the world does. I mean this is why we as humans and all animals work in probabilistic frameworks that are highly adaptable. Human intuition. Human ingenuity. We simply haven't figured out how to make models at that level of sophistication. Not even in narrow domains! What AI has done is undeniably impressive, wildly impressive even. Which is why I'm so confused why we embellish it so much. It's really easy to think everything is easy when we look at problems from 40k feet. But as you come down to Earth the complexity exponentially increases and what was a minor detail is now a major problem. As you come down resolution increases and you see major problems that you couldn't ever see from 40k feet. As a researcher, I agree very much with you. And as an AI researcher one of the biggest issues I've noticed with AI is that they abhor detail and nuance. Granted, this is common among humans too (and let's not pretend CS people don't have a stereotype of oversimplification and thinking all things are easy). While people do this frequently they also don't usually do it in their niche domains, and if they are we call them juniors. You get programmers thinking building bridges is easy[0] while you get civil engineers thinking writing programs is easy. Because each person understands the other's job only at 40k feet and are reluctant to believe they are standing so high[1]. But AI? It really struggles with detail. It really struggles with adaptation. You can get detail out but it often requires significant massaging and it'll still be a roll of the dice[2]. You also can get the AI to change course, a necessary thing as projects evolve[3]. Anyone who's tried vibe coding knows the best thing to do is just start over. It's even in Anthropic's suggestion guide. My problem with vibe coding is that it encourages this overconfidence. AI systems still have the exact same problem computer systems do: they do exactly what you tell them to. They are better at interpreting intent but that blade cuts both ways. The major issue is you can't properly evaluate a system's output unless you were entirely capable of generating the output. The AI misses the details. Doubt me? Look at Proof of Corn! The fred page is saying there's an API error. The sensor page doesn't make sense (everything there is fine for an at home hobby project but anyone that's worked with those parts knows how unreliable they are. Who's going to do all the soldering? You making PCBs? Where's the circuit to integrate everything? How'd we get to $300? Where's the detail?). Everything discussed is at a 40k foot view. [0] https://danluu.com/cocktail-ideas/ [1] I'm not sure why people are afraid of not knowing things. We're all dumb as shit. But being dumb as shit doesn't mean we aren't also impressive and capable of genius. Not knowing something doesn't make you dumb, it makes you human. Depth is infinite and we have priorities. It's okay to have shallow knowledge, often that's good enough. [2] As implied, what is enough detail is constantly up for debate. [3] No one, absolutely nobody, has everything figured out from the get-go. I'll bet money none of you have written a (meaningful) program start to finish from plans, ending up with exactly what you expect, never making an error, never needing to change course, even in the slightest.