I generally side with your skepticism, but there are verifiable domains with economic value such as drug discovery.

Construction isn't using LLMs any time soon like that. We don't even send our files in structured data because everyone's playing nose goes for liability and sending pdfs

Hadn't seen this quote before, amazing.

Also goes to show how hard it is to predict anything that is a massive change - these levels of changes are so infrequent that we can't rely on priors to predict the future.

What will be LLMs' social media?

I’m an expert software engineer. I would categorize something that multiplies my productivity by > 2x is transformative. AI has certainly done that and more for me

Did you experience the popularization of the WWW? There were massive disagreements even among smart tech-savvy people. We even had a stock market boom and just cycle associated with it.

Funnily, none of that mattered. What mattered is that enough ordinary people on the street found it useful, both for personal and business reasons. Ask yourself: are regular people using this tech, either as producers or consumers? And remember that this is the least powerful that it will ever be.

The TAM of electricity is 99.999% of humanity, yet there was 82 years between Alessandro Volta's Voltaic Pile experiments and the first electrical grid in the world, Pearl Street Station, New York City.

A farmer may have seen an LLM answering their questions about how to make a less dry chicken sandwich, but he hasn't seen a specifically designed and tested harness that autonomously manages a crop harvester. There is not a lot of work in this space right now, because programming and math are infinitely more verifiable, and the capabilities of never models would probably invalidate any usecases, but if the model progress slows down, all attention will go towards making them work for any aspect of production.

That polarizing take is magnified by our failure to teach how to manage discussions that contain controversy and disagreement. Then there is the fact that most people's idea of debate is some game of dominance and not to find solutions.

We're still in early stages, but in just 2 years AI has presented higher education with the single most disruptive change in decades. Now a computer can do your entire coursework for you, is available to converse on a broad range of topics, and can accomplish unaided huge open-ended problems.

> most other revolutionary technologies found their game changing, broad reaching applications by this point.

I think your timeline expectations are miscalibrated. The steam engine goes back centuries. Even in the mid 1700s to 1800s, advances in engine design were decades apart, and the logistics of building and deploying steam engines mandated an uptake in years, even in perfectly-fitted applications.

Like these applications where there were existing manual-labor solutions that not only fought the technology but were still competitive, AI and LLMs weren't born in a vacuum. In order to revolutionize, it must displace, and people are rightly finding ways to do their jobs in more productive ways that moderate adoption speed.

Can you name some other technologies that have exploded from 0 to billions of users in just 3 years?

It took the better part of a century for people to go from “hey look a steam engine” to “why don’t we put it on wheels?”.

Electricity took about half that time, 50 years, to go from “hey look a motor” to “why don’t we put them everywhere and build power grids?”

TV took 25 years from patent to mass market adoption.

The internet, 15 years from lynx to iPhone.

Google published the transformer architecture in 2017. We are currently at “oh hey a talking computer”.

Not sure about that. The nature of work changes if you have some[one|thing] that can work on it continuously forever. The goal of using AI shouldn't be to do the things a person does; it should be to do the things a person would be able to do if they had (practically) unlimited time.

This is one of the inflection points around working with AI. When your thinking shifts from "AI does what the person used to do" to "AI does something different that leads to the same outcome as before, but with all that cool stuff I'd love to have the time for", then the equation changes. For example, I will never write another app that doesn't have 100% test coverage again. I stress- and soak-test everything these days. That's changing how I write code - I need to write things in ways that have deterministic harnesses for things mutable state outside of my immediate control (like random numbers or datetimes or state loaded from a save) so that I can task AI with building a fuzzer that does tens of thousands of random tests on every push to see if anything broke. I couldn't do that in the past because it was always effort that a client wouldn't get enough value from to pay me to do it. I can do those things now. It's ace.

(Everywhere I put 'I' you can replace with 'AI of the Week')

Throw understanding images in there and it becomes less true in my experience. Sure they shit out code and search text well, but one infographic and they circle trying to rasterize it.

Are you breaking up your tasks and spawning new sessions for each, or are you just yoloing and letting it auto compact when it blows the context many times on such a long running task?

> It just requires instructions to continue working until the spec is complete

Try putting an LLM agent in a deterministic workflow without humans in the loop. My experience with this is not encouraging. Getting it to work requires sprinkling some context magic and hoping and praying the LLM does the right thing. More astrology or religion and less science. Great for use cases with humans-in-the-loop, but less than impressive when you need determinism and reliable operation.

>Computers, the Internet, Steam engine, Rail roads are much more reliable.

Because mostly they are doing a limit number of tasks in a non-generic manner.

Railroads had all kinds of problems at first, from iron rails bending and skewering the riders to engines exploding like high pressure bombs cutting down swaths of people like grain. People still used them with those risks because taking a wagon was worse.

The internet itself is a pretty simple concept. It's "just" packet routing. Now, to make that reliable at scale we've added an enormous amount of complexity to it. If you talk an old TCP stack and dump it on todays internet it you'd find yourself hacked pretty quickly. The internet is a generic packet router, but not a generic anything router.

Computers is where the reliability starts to fall apart. Once you go from specialized purpose to general purpose the size of the problem space explodes. You wouldn't get companies called things like Micro$lop (and these things were from before the AI days).

Then when you get to AI/LLMs and especially when you push into AGI you're creating a do anything now machine. And when you can do anything it takes a lot of training to eliminate the choices that end badly. You are the endpoint of 500 million years of training, for example.

>Can you do that with LLMs? It doesn't look that way.

Can anyone do it now? You're talking about low hanging fruit 200 years ago, but how much of that is left? How much harder are we going to have to fight for the next apple up?

> We are not people who write newspapers we are people who are directly involved in application of the technology, we posses a higher level of insight.

Cool, you're still a person who share their thoughts, just like the people who had similar reactions to when computers and the internet first came around. So many people were railing against the internet with "You'll never make payments over the internet", "it's too slow" and whatever. To the surprise of none of us nerds, the internet eventually ate the whole world and now large swaths of the population can't imagine life without it, for better or worse.

> Computers, the Internet, Steam engine, Rail roads are much more reliable.

You're comparing the reliability of huge ecosystems after they've gone mainstream, after decades, with something that is a couple of years old, hardly a fair comparison. Instead, compare where LLMs are today with the first years of computers, the internet and all that, and maybe you'll gain a new perspective on where LLMs are? None of those things were reliable initially and in many cases, involved actual human deaths before safety actually caught up.

Even when I got started with using the internet, which isn't that long time ago but around the modem-era, things were not so reliable or simple as they are today.

>but LLMs are not animals, animals learn from experience and LLMs don't.

I mean they kind of do by distilling said experience and putting it in the next model.

Current LLMs can't do it because building said world model is super expensive, anything that lowers that requirement brings us closer to continuous learning.

Also the models we train these days are typically generalized human text models. Animal models are a bit different because they won't be "word" models and they aren't going to be generalized text models like us humans use. In fact there is a story just today on HN about a user creating some rather simple transformer models to solve a number of the Arc-AGI problems not using (human) words at all. The reason you don't see more of this is most people aren't dumping compute into these kinds of issues, but instead going for the AGI prize.