Posted by saucymew 9/13/2025
The AI revolution has only just got started. We've barely worked out basic uses for it. No-one has yet worked out revolutionary new things that are made possible only by AI - mostly we are just shoveling in our existing world view.
I think AI value will mostly be spread. Open AI will be more like Godaddy than Apple. Trying to reduce prices and advertise (with a nice bit of dark patterns). It will make billions, but ultimately by competing its ass off rather than enjoying a moat.
The real moats might be in mineral mining, fabrication of chips etc. This may lead to strained relations between countries.
Having the cutting edge best model won't matter either since 99.9% of people aren't trying to solve new math problems, they are just generating adverts and talking to virtual girlfriends.
IIRC Sam Altman has explicitly said that their plan is to develop AGI and then ask it how to get rich. I can't really buy into the idea that his team is going to fail at this but a bunch of random smaller companies will manage to succeed somehow.
And if modern AI turns into a cash cow for you, unless you're self-hosting your own models, the cloud provider running your AI can hike prices or cut off your access and knock your business over at the drop of a hat. If you're successful enough, it'll be a no-brainer to do it and then offer their own competitor.
Electric utilities are also making bank, but it’s boring old electricity not some new AI electricity product.
If they actually reach AGI they will be rich enough. Maybe they can solve world happiness or hunger instead?
That's what normal people might consider doing if they had a lot of money. The kind of people who actually seem to get really wealthy often have... other pursuits that are often not great for society.
Maybe offloading software engineering thinking to AI will be a net good for humanity. If it atrophies engineering thinking in tech bros, perhaps they’ll stop believing that all societal problems can be solved by more tech.
I mean just a few days ago, we got "the left is the party of murder" - super helpful in terms of turning down the heat in the US. And of course that was without knowing what we now know about that situation...
we could have solved world hunger with the amount of money and effort spent on shitty AI
likely decarbonisation of the grid too, with plenty left over
Kill all people who are unhappy or hungry.
Kill all humans. :-)
There are still lots of currently known problems that could be solved with the help of AI that could make a lot of money - what is the weather going to be when I want to fly to <destination> in n weeks/months time, currently we can only say "the destination will be in <season> which is typically <wet/dry/hot/cold/etc>"
What crops yield the best return next season? (This is a weather as well as a supply and demand problem)
How can we best identify pathways for people whose lifestyles/behaviours are in a context that is causing them and/or society harm (I'm a firm believer that there's no such thing as good/bad, and the real trick to life is figuring out what context is where a certain behaviour belongs, and identifying which context a person is in at any given point in time - we know that psycopathic behaviour is rewarded in business contexts, but punished in social contexts, for example)
Anything is possible, well, except for getting the next season of Firefly
Edit: FTR I think that weather prediction is, indeed, solveable. We just don't have the computing power/algorithms that fully model and calculate the state.. yet
I’d even hold out hope for another season firefly <3
The model did its thing but there was still an aspect of interpretation that was needed to convert data to a story for a few minutes on TV.
For longer range forecasting the task was quite easy for the meteorologists, at least for the UK. Storm systems could be tracked from Africa across the Atlantic to North America and back across the Atlantic to the UK. Hence, with some well known phenomena such as that, my meteorologist friends would have a good general idea of what to expect with no model needed, just an understanding of the observations, obsessively followed, with all the enthusiasm of someone that bets on horses.
My forecasting friends could tell me what to expect weeks out, however, the exact time the rain would fall or even what day would not be a certain bet, but they were rarely wrong about the overall picture.
The atmosphere is far from a closed system, there only has to be one volcano fart somewhere on the planet to throw things out of whack and that is not something that is easy to predict. Predicting how the hard to predict volcano or solar flare affects the weather in a few weeks is beyond what I expect from AI.
I am still waiting for e-commerce platforms to be replaced with Blockchain dapps, and I will add AGI weather forecasting to the queue of not going to happen. Imagine if it hallucinates.
Will AI put bookmakers out of business? Nope. Same goes with weather.
All this "HN is so much better than other Social Media" is thus proved demonstrably false.
Weather systems exhibit chaotic behavior which means that small changes to initial conditions have far reaching effects. This is why even the best weather models are only effective at most a few weeks out. It’s not because we don’t understand how weather works, it’s because the system fundamentally behaves in a way that requires keeping track of many more measurements than is physically possible. It’s precisely because we do understand this phenomenon that we can say with certainty that prediction at those time scales with that accuracy is not possible. There is not some magic formula waiting to be discovered. This isn’t to say that weather prediction can’t improve (e.g I don’t claim we have the best possible weather models now), but that predictions reach an asymptotic limit due to chaos.
There are a handful of extremely simple and well understood systems (I would not call weather simple) that also exhibit this kind of behavior: a common example is some sets of initial conditions of a double-jointed pendulum. The physics are very well understood. Another perhaps more famous one is the three body problem. These two both show that even if you have the exact equations of motion, chaotic systems still cannot be perfectly modeled.
This is what you did say
> Then I don’t think you fully grasp the nature of weather.
Like - how the fck would you know? Even more so, why the fck does your ignorance and inability to think of possibilities, or fully grasp the nature of anything make you think that that sort of comment is remotely appropriate.
You have the uniquely fortunate position to never be able to realise how inept and incompetent you are, but putting that on to other people is definitely only showing everyone your ignorance to the facts of life.
And there was no reply - just downvoting people, like a champ...
Nothing to do with "inability to think of possibilities", it's impossible because of literal physics.
It's like saying perpetual motion machines could exist if we just think outside the box hard enough. No, we don't have them because thermodynamics.
Chaos theory only describes difficulties, in no circumstance does it describe things as "impossible"
If you don't understand the difference between the two terms, that would explain a lot.
What it means is that it takes more work (Computational Power) to properly model what's happening.
Just because you don't know the answer, doesn't mean there isn't one (as I have repeatedly pointed out).
I get it, you think that you already know everything that is to be known, but, the fact of the matter is you don't, nobody does, and pretending that you do is the real problem.
The issue isn’t computation. It’s measurement. It’s not possible to measure all of the factors that go into weather it will rain on a Tuesday at 3 pm 3 months from now (sorry for the terrible pun). It’s small perturbations in initial conditions.
The models we have are very coarse, and work for 24 hours (kind of, there are still extreme events that are difficult to be accurate about)
More sensors are being deployed this very second - which will present a finer grained picture.
It's not even at the rocket science part yet
A chaotic system in physics means "a tiny difference in initial conditions leads to large differences in outcome".
You cannot measure precisely enough to make a chaotic system predictable, even if the system is entirely deterministic and you understand all the physics involved.
It doesn't matter how many decimal places of accuracy you have or how many sensors there are, the error in the next decimal place will matter.
Theoretically, I suppose you'd eventually hit quantum effects and the fundamental limits of measurement. But I don't think quantum weather is likely to ever be a thing.
Anyways, the classic chaotic system is a double pendulum. If we can't predict the motion of two sticks, we're not predicting the weather lol.
As has been repeatedly pointed out, we have systems now that are accurate for 24 hours into the future, generally accurate for 72 hours, and mostly accurate for 120 hours
That's not "impossible because of chaos", that's "actually happening right now"
You're saying it cannot get any better, I'm saying it can
That's how wrong you are.
Ah I see. I misinterpreted the _you_ in this sentence (to mean me).
My main points still stand though:
1. weather is well understood to exhibit chaotic behavior (in the technical sense, not the colloquial sense) 2. there is an upper bound to accurate (edit: precise) weather forecasting the farther you predict into the future
As an aside, there was no need to get personal. I wasn’t the downvoter but that is very likely why the comment got flagged.
You've done nothing but be personal, complaining that it's being returned is hypocritical. You started making personal comments, not me.
> 2. there is an upper bound to accurate (edit: precise) weather forecasting the farther you predict into the future
Quick, everyone, halt all the research into weather prediction, we've already found all the answers. There's no need to look any further, none at all.
Actual answer: Currently we use a statistical model, that (as I previously pointed out currently degrades after about 3 days into the future)
There's absolutely nothing preventing us from making that more accurate, with better models, and algorithms (as I have been saying from the start)
I get that you don't have any understanding of the way that human knowledge is acquired, but that's no reason for you to jump on the internet and yell at people who do.
(Someone should tell Edison to stop at the 90th attempt of his lightbulb, it's clear that there are no answers to the problem he is faced with)
I’m genuinely sorry you feel that way. It wasn’t my intention.
> complaining that it's being returned is hypocritical
I was just responding to your complaint about the downvoting.
I really don't think you fully grasp anything at this point in the conversation
> Then I don’t think you fully grasp the nature of weather.
That aged well. /s
Is that what you meant by making it personal?
> That aged well. /s
It aged perfectly fine.
Yes you were making it personal, and yes it was you that was demonstrating the complete and utter lack of grasp of the subject.
So your claim is that the state of the art weather models are accurate at somewhere between 24h and 2 weeks (unclear based on the other sub threads) and continually improving. Based on this you extrapolate that given enough compute and sensors it would be possible to predict the weather with the same accuracy a month or more out. I think that’s a reasonable claim assuming that (1) the behavior of the system is deterministic and (2) the system behaves the same at both time scales.
Setting (1) aside, I claim that (2) is a wrong assumption. That weather exhibits chaotic behavior and that this likely puts an upper bound on prediction accuracy and the upper bound is less than 1 month.
The state of the art appears to be GraphCast [1] and FengWu [2]. These show promise out past 2 weeks when run against historical data. However, neither model is making actual weather predictions, and both are still in preprint (e.g. methodology has not been peer reviewed). This is super interesting and it’s possible my claim is incorrect, and that the upper bound is further out than the conventional 2 week limit.
[1]: https://arxiv.org/html/2504.20238 [2]: https://arxiv.org/abs/2304.02948
Your complaint is that I am applying your measure with the same effort that you did to me.
The current upper bounds are constantly being reviewed, just as with other active areas of research. With that in mind, we don't really know what the final upper bound might be, we can only say with any semblance of surety that the upper bound at any given point in time is limited by our imagination (read: our ability to create new and possible novel ways to model the future)
Not something that can be solved just by throwing more AI computation at it though.
I said "With the help of AI" no "Solved by AI"
The model is complex, and currently takes time on super computers to crunch through the numbers to give us an approximation, but that doesn't mean that it's never going to be fully modelled, or that we won't find a better way of approximating things where the long range forecasts are more accurate.
Currently the 24 hour forecast is highly reliable Three days reliable Five days is getting there ( it's still subject to change)
These things can be solved by throwing lots more compute at them (and the models improved)
Avalanche effect COMPLETELY PREVENTS certain things from being predictable.
There is only one thing that a person can never know, and that is the limit of their ignorance and incompetence.
innovator's dilemma
Absolutely with 150% certainty yes, and probably many. The www started April 30, 1993, facebook started February 4, 2004 - more than ten years until someone really worked out how to use the web as a social connection machine - an idea now so obvious in hindsight that everyone probably assumes we always knew it. That idea was simply left lying around for anyone to pick up and implement rally fropm day one of the WWW. Innovation isn't obvious until it arrives. So yes absolutely the are many glaring opportunities in modern capitalism upon which great fortunes are yet to be made, and in many cases by little people, not big companies.
>> if so, is a random startup founder or 'little guy' going to be the one to discover and exploit it somehow? If so, why wouldn't OpenAI or Anthropic etc get there first given their resources and early access to leading technology?
I don't agree with your suggestion that the existing big guys always make the innovations and collect the treasure.
Why did Zuckerberg make facebook, not Microsoft or Google?
Why did Gates make Microsoft, not IBM?
Why did Steve and Steve make Apple, not Hewlett Packard?
Why did Brin and Page make Google - the worlds biggest advertising machine, not Murdoch?
Also, there was Classmates.com. A way for people to connect with old friends from high school. But it was a subscription service and few people were desperate enough to pay.
So it's wasn't just the idea waiting around but idea with the right combination of factors, user-growth on the Internet, etc.
And don't forget Facebook's greatest innovation - requiring a .edu email to register. This happened at a time when people were hesitant to tie their real world personas with the scary Internet, and it was a huge advantage: a great marketing angle, a guarantee of 1-to-1 accounts to people, and a natural rate limiter of adoption.
The giant win comes from many stars aligning. Luck is a factor - it's not everything but it plays a role - luck is the description of when everything fell into place at just the right time on top of hard work and cleverness and preparedness.
Google Search <-- AltaVista, Lycos, Yahoo
Facebook <-- MySpace, Friendster
iPod <-- MP3 players (Rio, Creative)
iPhone <-- BlackBerry, Palm, Windows Mobile
Minecraft <-- Infiniminer
Amazon Web Services <-- traditional hosting
Windows (<-- Mac OS (1984), Xerox PARC
Android <-- Symbian, Windows Mobile, Palm
YouTube <-- Vimeo, DailyMotion
Zoom <-- WebEx, Skype, GoToMeeting
mp3 players were commodity items, you could buy one for a couple of dollars, fill it up with your favourite music format (stolen) and off you went.
Phones too - Crackberry was the epitome of sophistication, and technological excellence.
Jobs/Apple didn't create anything "new" in those spheres, instead he added desireability, fancy UX that caught peoples' attentions
It had Geocities, chatrooms and messengers, as well as, for a while, a very strong search engine.
In that scenario, everyone makes money: OpenAI, Google (maybe Anthropic, maybe Meta) make money on the platform, but there are thousands of companies that sell solutions on top.
Maybe, however, LLMs get commoditized and open-source models replace OpenAI, etc. In that case, maybe only NVIDIA makes money, but there will still be thousands of companies (and founders/investors) making lots of money on AI everything.
Every use case I have for LLMs is satisfied with copilot, but even then if it costs like $5 a month to access someday, I’d just as soon not have it. Let alone the subsequent spending.
What LLMs are absolutely not useful for, in my opinion, is answering questions or writing code, or summarising things, or being factual in any sense at all.
That’s kinda happening, small local models, huggingface communities, civit ai and image models. Lots of hobby builders trying to make use of generative text and images. It just there’s not really anything innovative about text generation since anyone with a pen and paper can generate text and images.
>The article "AI Will Not Make You Rich" argues that generative AI is unlikely to create widespread wealth for investors and entrepreneurs. The author, Jerry Neumann, compares AI to past technological revolutions, suggesting it's more like shipping containerization than the microprocessor. He posits that while containerization was a transformative technology, its value was spread so thinly that few profited, with the primary beneficiaries being customers.
>The article highlights that AI is already a well-known and scrutinized technology, unlike the early days of the personal computer, which began as an obscure hobbyist project. The author suggests that the real opportunities for profit will come from "fishing downstream" by investing in sectors that use AI to increase productivity, such as professional services, healthcare, and education, rather than investing in the AI infrastructure and model builders themselves.
I used to be the biggest AI hater around, but I’m finding it actually useful these days and another tool in the toolbox.
I think we'll see a ton of games produced by AI or aided heavily by AI but there will still be people "hand crafting" games: the story, the graphics, etc. A subset of these games will have mass appeal and do well. Others will have smaller groups of fans.
It's been some time since I've read it, but these conversation remind me of Walter Benjamin's essay, "The Work of Art in the Age of Mechanical Reproduction".
Is it a large market though?
The first automated-server restaurants (Horn and hardart) appeared in the 1930s during the depression. They were popular because they were cheap.
Far from being the wave of the future, they went out of business in the 1950s when people started having disposable income.
Part of the reason we accept slop, impersonal service and mass produced crud is not because "demand" is indifferent to it, but because disposable income is so often politically repressed, meaning the market is forced to prioritize price.
Specifically, you can read about Automats, which were basically early vending machines: https://en.wikipedia.org/wiki/Automat
The point is that the automation of the customer service part of restaurants existed before and disappeared for ~60 years.
That is fairly insignificant segment of the market.
I remember back in 2004, my first project was testing a teleconferencing system. We set up a huge screen with cameras at one of our subsidiaries and another at the HQ, and I had a phone on my desk with a built-in camera and screen. Did the company roll out the system? No, it didn’t. It was just too expensive. Did they make a fortune from that experience? No, they didn’t. But I’m pretty sure all companies in the knowledge industry that didn’t enable video calls and screen sharing for their employees went out of business years ago...