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As long as the term “AI” means by-and-large LLMs with additional features sprinkled on top, the answer is no. More likely (without careful vetting by the folks aggregating these models) is that the quality will go down as more and more AI-generated output gets subsumed into these models.
Even without that particular problem, LLMs-as-AI can only give us probabilistic outputs based on inputs; and by definition they’re reliant on humans to provide the training data for their model. Without specialized knowledge or training on that knowledge (And even with it, viz. Meta’s engineering), we don’t have to worry about AI itself. We do have to worry what investors who are looking for outsized returns will do to get those returns, job market be damned.
The problem for us isn’t that AI will take our jobs; it’s that snake-oil salesmen can sell the idea that AI will take our jobs, investors buy into it, companies try it, fire their folks, the snake-oil salesmen IPOs, the companies that bought into this idea implode in some form or fashion, and the salesmen have already taken the money and ran. Of course, we still lose our jobs, but maybe (!) we get them back when this all fails?
This assumes that there aren't algorithmic breakthroughs which reduce training/inference costs by several OOMs.
How much do these models need to do before people throw their hands in the air and say, ok this is happening. The Erdos unit distance problem, which as far as I understand was approached by multiple competent mathematicians was solved by a frontier model. Sure people argue there was no novelty there (I cannot comment as a non-mathematician) but it feels like they can draw lines laterally from deep knowledge in different fields (in this case combinatorics and algebraic number theory I believe) and solve problems.
Now if you have millions of instances running in parallel, all "probabilistic", working on frontier AI research I really don't see the blocker (and believe me I wish I did).
What is "this"? Most people arguing against some of the more fervent predictions and promises of "inevitability" are people who are using these models in day to day - they see what the models can do, and what they struggle at.
> Now if you have millions of instances running in parallel, all "probabilistic", working on frontier AI research I really don't see the blocker (and believe me I wish I did).
My genuine prediction is that you'll get a lot of early results simply because you're applying attention to some low hanging fruit of problems, but then it will drop off due to the cost of tokens and the low rate of return. This doesn't mean that the models are especially capable of novel thought, just that we haven't algorithmically brute forced a problem with known solutions.
We would be seeing more success cases if the promises were true, setting aside AGI, human replacement, etc. We would see more, better products with more features that people would use. We wouldn't be having any arguments. The human replacement presupposes the models work in ways that they don't, and until proven otherwise, can't. I've watched those who embrace it fully flounder around on projects, some have lost their mind from the constant LLM validation, and I've seen companies go all in and then pull back based on both cost and efficacy over the last year.
I'm still waiting for the success case examples applied on a scale that would make any of the predictions come true.
Yes, if one must assume something it is generally fair to assume that things will continue as they are. Research breakthroughs do happen, but they are not something for which you can predict the timing.
Open AI et al are hemorrhaging absurd amounts of money. It's not clear whether there will ever be a good balance between cost, value, and price.
Lots of companies are already questioning the value they get from LLMs at current prices which are obviously not enough to generate profits.
So I don’t see accuracy declining at least for programming.
How do those chat bots discern that the ‘web searches’ they’re using are returning human generated information only that’s been vetted instead of LLM output?
Welcome to the postmodern internet. It's vibes all the way down.
Or, it eventually becomes clear to enough people that the AI companies aren't going to make enough money to justify their valuations, so the asset bubble bursts, the economy crashes, and we lose our jobs.
The tremendous growth in earnings meant some fake excess head count number was viable.
Google faced an activist investor who practically forced sundar to fire a bunch of people. This is what’s coming for big tech if this AI thing blows up. Apple Is safe because they are clever and saw this from a mile away.
Investors want their cake and they will eat it too.
I am not completely sure what you are saying here, but it sounds like a variation of the "it's just a stochastic parrot" argument, which is reductionist. The human brain is also just a bunch neurons firing.
Nope. This isn't how it works.
AI progress has largely been synthetic and has produced leaps and bounds capabilities increases in the last couple of years.
Sorry.
The only thing that matters is if LLMs with sufficient scaling can become frontier AI researchers kicking off the exponential. Everything else is transient noise.
I agree with your sentiment (about the noise), however I think this over simplifies it a bit. We may get AI that is super-human at frontier research and dramatically accelerates the pace, and still have to wait decades before it disrupts the job market (or maybe never displaces all work).
For one, the answer may depend on material science and chip manufacturing that can take a very long to build out a supply chain for even with super AI help.
And we may just find that the human mind is way more capable than we thought and even with accelerating research it's just a harder problem than anyone expected, even algorithmically.
I expect it to be a bit of both, and from ~2015 - 2025 I was in the "AI is coming for all our jobs" camp. My perspective changed last year after doing a deep dive into latest science on the human brain. (I've kept a very close eye on AI dev progress for 12+ years.
I don't see why that's the case when you have super-human researchers on tap. There are indeed physical (supply chain-y) issues to deal with but isn't the whole point that: 1. Super-human at AI research + scaling to millions of instances will probably result in super-intelligence in everything which is not AI research. (a subset of which is white-collar work) 2. Use that super-intelligence to solve any supply-chain issues you might be facing.
> And we may just find that the human mind is way more capable than we thought and even with accelerating research it's just a harder problem than anyone expected, even algorithmically.
I hope so but whenever I do, I feel like I'm coping hard and not dealing with the facts.
I'm not saying we're there yet - I'm saying the trend lines are clear.
I think this is where a lot of people's thinking goes awry. Unlimited intelligence doesn't mean unlimited resources or instantaneous implementation.
Hahahaa this is what AI psychosis looks like
I think we know the answer to that already - LLMs show no sign of improving intelligence and instead providers are going down the ‘agentic’ rabbit hole.
There are too many things missing, like a world model, understanding, and taste (in the sense of knowing what is good and what is not good).
I'm not sure where you're getting this. I don't work at Anthropic but Fable (Mythos) seems demonstrably smarter than Opus for pretty much any definition of smarter and they claim that Opus was used heavily in Mythos development (yeah I know take this with a massive pinch of salt).
Either way if the models are indeed helping development, even on the engineering, you can iterate on models faster and even if they're not contributing to core research yet you still have a baby exponential by improving the engineering.
1. Cutting edge LLMs developing ASI/AGI. 2. AIs doing general knowledge work
The second world will be achieved far before the first world is achieved. And as the first path gets develolped, the second path becomes cheaper and cheaper to run inference on along with being democratized which reduces the margins for the cutting edge companies. It seems like a mad dash to go as far as possible until 90% of general work can be automated with more cheaply available tech
The cost is already outrunning the benefit to a massive amount, and the predicted expotential is not here yet. I predict it'll always be around the corner, a $1T model won't get there, but it will "look promising", but we'll sadly run out of money for the $10T or $100T model..
What if the answer is flatly: no? All that other stuff starts to matter a lot then.
Predicating your business decisions on a potential breakthrough that may never come is frankly insane. Imagine if at the dawn of the car industry Ford decided that it's actually a race to build the first flying car and nothing else matters.
Napoleon got sent to Elba. Hitler ate a bullet. Your average tech CEO will still have more money than they can spend.
The more pernicious effect I’ve been seeing is that we’re living in the golden age of LLMs, but eventually that’ll fade. Tokens are subsidized and cheap, model capabilities leap forward regularly, and there’s competition driving it all. But even now there’s stories about frontier models suddenly becoming less capable, or providers switching to usage-based billing, and new model releases feel a bit more sluggish and less dramatic. (Fable/Mythos notwithstanding.)
Eventually the models are going to settle into a rut of being just “good enough” to earn a living rather than all this hoopla. A lot of people will be re-hired. And we’ll do it all again for the next wave.
Only a Tech CEO speaks in absolutes, it seems.
Most, if not nearly all, of these teams have little to show ROI wise and the music on the AI bubble is slowing dramatically. They went from seemingly unlimited budgets and headcount when CEOs said “get me some of that AI” to some really uncomfortable scenes playing out know as the same CEOs realize this has cost a fortune with little to show for it.
Took that nonsense to Capitol Hill, trying to tell a bunch of politicians who knew damn well they are only there as long as they can keep their voters employed. They could have asked their own AI what happens when employment reaches 40-50%. Hint: it's never good. They were going to become another problem the government had to solve.
Also, UBI is non-starter no matter what Sam Altman believes.
Do you mean it's a non-starter in the current political climate? Or that you personally just don't think it will work?
Until AI no longer needs human supervision, it's more profitable to tax as many employees as possible.
https://econlab.substack.com/p/we-can-finally-say-ai-isnt-ki...
If that makes me a bad person, fine. If a few CEO's wind up working at 7-11 to make rent money, all the better.
There are CEOs who have only ever failed abysmally their entire careers, and they generally only ever make more money. Accountability is for losers.
personally, I am collecting 3 salaries working remotely. For one of the jobs, I am tasked with hiring other devs but i dont put the effort in as i dont see a point. i just say i can't find a decent engineer and why should i when a frontier models can do most of their work? in our job postings we see thousands of applicants in a very short period of time, i just do these multi stage interviews with a rotation of candidates to basically buy time while i work on another job
i see that things are getting very desperate and i feel for those that are still struggling to find SWE jobs, AI is absolutely doing a number and the gap is going to increase not decrease.
Which is exactly what every one on HN with a working brain predicted a decade+ ago.
It's time for the program to end.
enterprises thrive when they have the freedom to choose who to hire without government interference
If you believe AI will 10x you're developers you've drunk the kool-aid, if you believe AI will have no impact on your developers then you're being stubbornly ignorant.
I'm a staff engineer and the amount of work and even troubleshooting the latest crop of LLMs can do at my behest is jaw-dropping.
I perused your comment history, and you refer to models as 'ChatGPT', which doesn't really inspire much confidence that you know what you're talking about.
People keep telling you that you're behind and using old technology and that the most recent models are really good and a sea-change because, well - they are.