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Comment by CuriouslyC | Hacker News Reader

I was at an AI meetup, and I talked to someone who was in charge of the emerging technology division at a VC firm. I asked him why focus so hard on AGI, when AI tools are already looking quite impressive and are a much clearer area to focus investment on. His answer was that AGI was "easy" which I laughed at (in a good natured way) and tried to get him to elaborate on, at which point he started to get uncomfortable and made an excuse to go talk to someone else.

This same fellow was big on autonomous swarms for problem solving, but when I asked him what the problem autonomous swarms were supposed to solve that you couldn't solve more easily and quickly by a LLM talking to itself, he didn't have an answer.

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Remember that these same VC firms had large crypto and blockchain divisions and were hosting lavish conferences dedicated to it. Regardless of what their job title is, if someone can't back up their talk with real world experience with the tech, there's no reason to take them seriously.

I am patiently waiting for the industry and VCs to pivot their "visions" to "vertical AI", then to "targeted ML applications", and "knowledge-assisted automation systems for the manufacturing and logistics industries". They really burned a lot of money and energy on stuff noone seems to want or need. That money is going to run out soon.

As the old saying says “The market can stay irrational longer than you can stay solvent”

For some reason, VCs got super hype about foundation model companies, but from my perspective being very deep in this area is that there's zero moat there with the prevalence of open source, so it's a very stupid area to burn massive amounts of cash in.

Targeted AI applications and virtual "executive assistant" agents are going to be huge though.

Everyone is building nonsense betting that openai’s next real innovation will validate their idea and they’ll be ahead of the curve.

That’s all it really is

You make it sound silly, but it does seem to make good business sense to me. I'd argue that it seems like they learned from The Bitter Lesson[0] and instead of trying to manually solve things with today's technology, are relying on the exponential improvement in general purpose AI and building something that would utilize that.

On a somewhat related note, I'm reminded of the game Crysis, which was developed with a custom engine (CryEngine 2) which was famously too demanding to run at high settings on then-existing hardware (in 2007). They bet on the likes of Nvidia to continue rapidly improving the tech, and they were absolutely right, as it was a massive success.

[0] http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Yeah it’s a real valid business strategy.

It’s also silly.

The crytek case is similar, but there’s a big difference. Crytek was betting that performance of hardware will increase. A lot of AI startups are betting that future LLMs will have an new different, fairly vague, capabilities (AGI, whatever that actually means)

I'd also add that what Crysis did was pretty typical at the time. It was an era when new computers were a bit dated in a few months, and then obsolete in a couple of years. Carmack/ID Software/Doom was a more typical example of this, as they did it repeatedly and regularly, frequently in collaboration with the hardware companies of the time. But there was near zero uncertainty. There was a clear path to the goal down to the point of exact expected specs.

With LLMs there's not not only no clear path to the goal, but there's every reason to think that such a path may not exist. In literally every domain neural networks have been utilized in you reach asymptotic level diminishing returns. Truly full self driving vehicles are just the latest example. They're just as far away now as they were years ago. If anything they now seem much further away because years ago many of us expected the exponential progress to continue, meaning that full self driving was just around the corner. We now have the wisdom to understand that, at the minimum, that's one rather elusive corner.

"In literally every domain neural networks have been utilized in you reach asymptotic level diminishing returns."

Is that true, though? I think of "grokking", where long training runs result in huge shifts in generalization, only with orders of magnitude more training after training error seemed to be asymptotically low.

This'd suggest both that there's not that asymptotic limit you refer to - something very important is happening much later - and that there are potentially some important paths to generalization on lower amounts of training data that we haven't yet figured out.

I think training error itself has diminishing returns as a metric for LLM usefulness.

A lower error, after a certain point, does not suggest better responses

Did he mean "AGI is easy to build" or "AGI is easy to sell"?

My impression was easy to build, as he had an engineering background. It seemed like he expected AGI was ready to emerge fully formed like Athena from the head of Zeus.

Could also have meant "easy" (no-brainer) to invest in, whether or not that's actually true.

My take would be that AGI is inevitable, and it's your choice if you want to be a part of making it (or deadend branches along the way) or not. I take Ray Kurzweil's predicted timeline as being the most sensible.

I just don’t think Ray Kurzweil understands these systems well enough. His views aren’t of much substance anymore.

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