| > Not to mention, in your world, gatekeeping the machines would instantly become the most profitable venture possible. Yes, it would! That's why frontier labs don't open-source their models :) The point is that the technology is already too democratized for anyone to hold onto it. Google had chatbot LLMs in 2019 and tried to keep them under wraps, how many years did that buy them? > Do you really need the problem between chair and keyboard will be needed after another 10? And do you really think that in 20 years time that we will all be paid to prompt increasingly advanced and independent LLMs? I think that things are going to get so much cheaper that we'll still be paid more than enough. > The way automation is going, knowledge work will be automated first before any physical production processes are. So far, LLMs are great and all, but they only really "fill in the blanks." That's a fundamental limitation of the entire concept of modelling in general; you cannot generalize to out-of-distribution inputs. The bottleneck is going to end up being human beings no matter which way you slice it. Because the bottleneck will be people, more and more of them will be hired, even though each individual is incredibly productive. This is also called Jevon's paradox, when making a resource less expensive leads to overall market growing. > You are pretty much just describing some sort of fantasy automated communism. If you went back a thousand years ago and told someone carrying a bucket full of water that one day pipes would run across the civilized world and water would literally be free basically everywhere, they might react the same way. If VLA-driven robots start reducing manufacturing prices, is it so unreasonable to slowly expect more and more things to go that direction? |
They were hardly the only ones in the space. OpenAI has been around since 2015. GPT-3 was released in 2020 and ChatGPT in 2022. Not to mention, I wouldn't call something produced by a handful of megacorporations worldwide particularly democratized. In fact, Google's transparency is what allowed it to be democratized, because it published its findings about transformers publicly.
> So far, LLMs are great and all, but they only really "fill in the blanks." That's a fundamental limitation of the entire concept of modelling in general; you cannot generalize to out-of-distribution inputs. The bottleneck is going to end up being human beings no matter which way you slice it.
This is a laughably naïve take especially when LLMs have a) been trained on quite literally all the data the world can provide and b) are being trained more and more using reinforcement learning techniques - which don't rely on data at all and instead on producing emergent behaviour from a set of ground rules. With every new release their agentic capabilities improve and they become more independent, requiring only the impetus to get going.
> This is also called the Jevons paradox, when making a resource less expensive leads to overall market growing.
Oh yes, there will definitely be more software. That is guaranteed. What is not guaranteed is how many humans will be involved in making it. Just as more coal is being mined than ever but fewer people are involved in it. Efficiencies in coal mining aren't what made the average coal miner's working conditions or income better, regulations are.
> If you went back a thousand years ago and told someone carrying a bucket full of water that one day pipes would run across the civilized world and water would literally be free basically everywhere
If you told a Roman this, they would not be as surprised as you would think as aqueducts already existed back then. They would be more surprised that the common man had the ability to vote in most countries. I doubt it will stay that way with improvements in AI, at least not without a great reduction in population.