Comment by p-o
16 hours ago
By the looks of it, 2026 might be the year where reality and fiction will finally collide with AI and we'll be able to see if all the hype was warranted.
But like all the previous hype, most of the people that were the loudest won't say they were wrong, and they'll move to the next thing, pretending like they never were the one that portrayed AI as the holy Graal.
There are all sorts of algorithms in use that were once thought of as AI, but transitioned to being mere algorithms well before they entered public awareness, if they did that at all. Some are still useful and used everywhere, but they have never been thought of as AI by the public. For them, AI is a term that has long been reserved for some far-off, sci-fi future.
LLM's are not artificial general intelligence (i.e. not sci-fi AI). Why haven't they transitioned to being mere algorithms by now? Why is the public being told AI is finally arriving when it's really just another algorithm?
We have some truly slick and shady corporations involved in the bubble right now and they're marketing LLM's like tobacco. LLM's have been pushed out, at immense cost, to the public in a way that makes them more directly accessible to average people than any past algorithm. Young children can ask a LLM to do their homework for them. Middle managers can ask a LLM to create a (shitty) ad campaign for them. Corporations have gone to tremendous expense to make that widely available and, for the moment, mostly free. They seem to be following the Joe Camel school of marketing. Get them hooked while they're young so they come to you first when they're older! The only difference is that nobody is stepping in to stop the new Joe Camel from handing out free samples to kids.
Then there's the "go big" aspects of the bubble. The major competitors are trying to out-spend each other to dominance, but the suckers are so colossally big that their bubble is affecting global GPU, memory, and storage prices. This bubble is going to stress power grids wherever it operates and do considerable environmental harm. The financial games being played behind the bubble are absolutely stupid. The results, so far, are tantalizing for billionaires. LLM's offer the promise of being able to fire all their pesky and annoying human workers. It won't deliver on that, and none of these companies is ever going to make enough to pay their debts. There might be "too big to fail" government bailouts, but there are going to be some big bankruptcies too.
Useful algorithms will come out of all this, a lot of tears too, but not "AI".
Are you saying "algorithm" as a funny way to say things deemed "dumb" or algorithm as in any turing system?
Do you think ai can never even conceptually become equivalent to a human or merely that the current crop is not there yet?
AI is real but the socio-political environment is far from conductive to some form of productive use of it - as opposed to using it as a war-machine - AI isn't going to fail in that role but very few will be happy about it.
I mean, disillusionment is the least of my worries.
> and we'll be able to see if all the hype was warranted.
Umm, what? For the past 3 years, every year I've said something along the lines of "even if models stop improving now, we'll be working on this for years, finding new ways to use it and make cool stuff happen". The hype is already warranted. To have used these tools and not be hyped is simply denial at this point.
Maybe AI is useful to you, but the US economy is currently buoyed by promises of AI replacing the workforce across the board.
Most of Mag-7 are planning to spend over 500B on capex this year alone on building out datacenters for AI pipelines that have yet to prove that it can generate a sustainable profit. Yes, AI is useful in some environments, but the current pricing is heavily subsidized. So my point stand, the hype is not warranted.
> but the US economy is currently buoyed by promises of AI replacing the workforce across the board.
Still don't understand what's the end goal here. Assuming they don't deliver, then there are billions of investments that will go bust. Assuming they deliver, millions lose their jobs and there's going to be a bloodbath on the streets.
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I think our little corner of the world has a distorted view of AI in that it is actually proving useful for us. Once they passed a certain level of usefulness... I remember when they were still struggling just to output syntactically correct code, you know, like, 18 months ago or so... they became a useful tool that we can incorporate.
But there's a lot of things playing out to our advantage. Vast swathes of useful and publicly available training data. The rigorous precision of said data. Vast swathes of data we can feed it as input to our queries from our own codebases. While we never attained the perfect ideal we dreamed of, we have vast quantities of documentation at differing levels of abstraction that the training can compare to the code bases. We've already been arguing in our community about how design patterns were just level of abstraction our coding couldn't capture and AI has access now to all sorts of design patterns we wouldn't have even called design patterns because they still take lots of code to produce, but now for example, if I have a process that I need to parallelize it can pretty much just do it in any of several ways depending on what I need at that point.
It is easy to get too overexcited about what it can do and I suspect we're going to see an absolute flood of "We let AI into our code base and it has absolutely shredded it and now even the most expensive AI can't do anything with it anymore" in, oh, 3 to 6 months. Not that everyone is going to have that experience, but I think we're going to see it. Right now we're still at the phase where people call you crazy for that and insist it must have been you using the tool wrong. But it is clearly an amazing tool for all sorts of uses.
Nevertheless, despite my own experiences, I persist in believing there is an AI bubble, because while AI may replace vast swathes of the work force in 5-20 years, for quite a lot of the workforce, it is not ready to do it right this very instant like the pricing on Wall Street is assuming. They don't have gigabytes of high-quality training data to pour in to their system. They don't have rigorous syntax rules to incorporate into the training data. They don't have any equivalent of being guided by tests to keep things on the rails. They don't have large piles of professionally developed documentation that can be cross-checked directly against the implementation. It's going to be a slower, longer process. As with the dot-com bubble, it isn't that it isn't going to change the world, it is simply that it isn't going to change the world quite that fast.
Leaving aside the economic shitshow and other things.
I think you're right but for the wrong reasons wrt sustainable profit.
Specifically, overcounting how much it will cost in 5 years to run AI because you're extrapolating current high prices, and at the same time undercounting how the demand will drive efficiency gains.
i think the point is AI has to go much further and faster than it has in the past 3 years to justify the investments being made from the hype. The hype did its job now the AI industry has to execute and create the returns they promised. That is still very much up in the air, if they can't then the tech was over hyped.
This.
It's high time to stop accumulating debt while providing free picture of pelicycles, just charge the full cost for them - enough to generate profits and pay back debt.
What we see now is literally burning money and energy to generate hype. The only true measures of success are financial and macroeconomic. If the hype is real, there should be no problem for the mighty AI to generate debt-free profits for its providers while the overall price level in the US goes down.
We observe the exact opposite which makes the AI hype act only as market manipulation for capital misallocation.
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> most of the people that were the loudest won't say they were wrong
I was so expecting to find this wind-up aimed at those peddling the "AI is hype" laziness.
It's laziness because they have little CS fundamentals to base such claims on, and the deductions can be made, just not clearly to people who need to study a lot more.
It's like watching an invisible train (visible to those with strong CS) rolling down the tracks at a leisurely pace. Those sitting in their stalled car on the tracks are busy tweeting about "AI HPY PE TRAIN." Until it wrecks their car, the gimmick is free oxygen. It's a lot easier to write articles than it is to build GPUs and write programs.
> It's laziness because they have little CS fundamentals to base such claims on
So, what CS fundamentals do you need to evaluate if AI is the real thing, or will disappoint in the future? Until a few months ago, coding agents were met with skepticism, until Anthropic introduced their new model and, with it, a hype train that cannot be rationally justified. Look, SOTA LLMs, and coding agents in particular, are impressive. However, current predictions about the future of software development (and the world in general) are speculative. There is little to no data showing whether AI can deliver on its promises. How could there be in this short time frame? No one knows what the future will hold, no one knows how coding agents will be integrated into our work life and everyday life in the long run, or what hard limitations they will reveal. No one can tell you how professions will change in the coming years; every prediction is purely speculative, and anyone making prophecies is either trying to cope with the uncertainty themselves or has some stakes in the AI bet. It would be nice if people were actually humble enough to admit that they have no idea what will happen in the future, instead of writing the hundredth doom and gloom post.
> However, current predictions about the future of software development (and the world in general) are speculative.
It's amazing to me how those willing to seize on the speculative nature of any ANY uncertainty cannot recognize the inherent uncertainty of the inverse.
> what CS fundamentals do you need
1. Tarski's undefinability theorem 2. Gödel's incompleteness theorems 3. Curry Howard correspondence
And a lot of exposure to deductive reasoning, vague ideas of automated theorem proving and formalization.
I won't pretend its easy, but let's be clear, a small fraction of people who know things are being forced to entertain the hysteria of a vast majority who are unwilling to know things and just go around beating their chests and will continue doing so until the train hits them.
There are 2-3 minor architectural changes in between now and what I would identify as a completely unbounded AGI with clearly discernible dynamic, self-defined objective functions and self-defined procedures for training and inference. It can be done in megabytes. Oh god. Get me out of this forum. I wish to return to my code editor.
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