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Reflections on AI at the End of 2025
181–190 of 383 posts
Re: Reflections on AI at the End of 2025
#182Earlier quoted context omitted.
Depends if they use lobotomized bots like Grok...
>> So the number of anti-vaxxers is going to plummet drastically in the following decade, I guess. > Depends if they use lobotomized bots like Grok... What are you on about? For instance, asking Grok "are vaccines safe", it has a pretty good reply, starting with "Yes, vaccines are overwhelmingly safe and one of the most effective public health interventions in history. Extensive scientific evidence from decades of re…
The issue is what happens when @catturd2 quotes this and tweets Elon about Grok not toeing the party line about vaccines
Re: Reflections on AI at the End of 2025
#183This article does little to support its claims but was a good primer to dive into some topics.
They are cool new tools use them where you can but there is a ton of research still left to do. Just lols at the hubris silicon valley will make something so smart it extincts humankind. It'll happen from the lack of water and heated planet first :)
The stocastic parrot argument is still debated but more nuanced than before. Although the original author still stands by the statement. Evidence of internal planning per model. Anthropic Attribution Graphs Research with some rhyming did support it but gemma didn't.
The idea of "understanding" is still up for debate as well. Sure, when models are directly trained on data there is representation. Othello-GPT Studies was one way to support but that was during training so some interal representation was created. Out of distribution task will collapse to confabulation. Apple's GSM-Symbolic Research seems to support that.
Chain of thought is a helpful tool but is untrustworthy at best. Anthropic themselves have showed this https://www.anthropic.com/research/reasoning-models-dont-say...
Re: Reflections on AI at the End of 2025
#184Earlier quoted context omitted.
If the tech plateaus today, LLM plans will go to $60-80/mo, Chinese-hosted chinese models will be banned (national security will be the given reason), and the AI companies will be making ungodly money. I'm not gonna dig out the math again, but if AI usage follows the popularity path of cell phone usage (which seems to be the case), then trillions invested has a ROI of 5-7 years. Not bad at all.
OpenAI would still lose money if the basic subscriptions were costing $500 and they had the same amount of subscribers as right now. There's not a single model shop who's ever making any money, let alone ungodly amounts.
This is literally lies and total bullshit. They’d be making insane profits at those prices.
They don’t have to spend all their cash at once on the 30GW of data centers commitments.
Why go on the internet and tell stupid lies?
Re: Reflections on AI at the End of 2025
#185Earlier quoted context omitted.
> No, that doesn’t make you a skeptic in this context. That's good to hear, but I have been called an AI skeptic a lot on hn, so not everyone agrees with you! I agree though, there's a certain class of "AI denialism" which pretends that LLMs don't do anything useful, which in almost-2026 is pretty hard to argue.
On the other hand, ever since LLMs came on the scene, there’s been a vocal group claiming that AI will become intelligent and rapidly bring about human extinction - think the r/singularity crowd. This seems just as untenable a position to hold at this point. It’s becoming clear that these things are simply tools. Useful in many cases, but that’s it.
The AI doomers have actually been around long before LLMs. Discussion about AI doom has been popular in the rationalist communities for a very long time. Look up “Roko’s Basilisk” for a history of one of these concepts from 15 years ago that has been pervasive since then.
It has been entertaining to see how Yudkowsky and the rationalist community spent over a decade building around these AI doom arguments, then they squandered their moment in the spotlight by making crazy demands about halting all AI development and bombing data centers.
Re: Reflections on AI at the End of 2025
#186Earlier quoted context omitted.
No, that doesn’t make you a skeptic in this context. The LLM skeptics claim LLM usefulness is an illusion. That the LLMs are a fad, and they produced more problems than they solve. They cite cherry picked announcements showing that LLM usage makes development slower or worse. They opened ChatGPT a couple times a few months ago, asked some questions, and then went “Aha! I knew it was bad!” when they encountered their…
> They cite cherry picked announcements showing that LLM usage makes development slower or worse. They opened ChatGPT a couple times a few months ago, asked some questions, and then went “Aha! I knew it was bad!” when they encountered their first bad output instead of trying to work with the LLM to iterate like everyone who gets value out of them. "Ah-hah you stopped when this tool blew your whole leg off. If you'd s…
> "Ah-hah you stopped when this tool blew your whole leg off.
Yes, because everyone who uses LLMs simply writes a prompt and then lets it write all of the code for them without thinkng! Vibecoding amirite!?
Re: Reflections on AI at the End of 2025
#187Earlier quoted context omitted.
"Skeptics" is also a loaded term; what does it actually mean? I find LLMs incredibly useful for various programming tasks (generating code, searching documentation, and yes with enough setup agents can accomplish some tasks), but I also don't believe they have actual intelligence, nor do I think they will eviscerate programming jobs, the same way that Python and JavaScript didn't eviscerate programming jobs despite l…
No, that doesn’t make you a skeptic in this context. The LLM skeptics claim LLM usefulness is an illusion. That the LLMs are a fad, and they produced more problems than they solve. They cite cherry picked announcements showing that LLM usage makes development slower or worse. They opened ChatGPT a couple times a few months ago, asked some questions, and then went “Aha! I knew it was bad!” when they encountered their…
You can acknowledge both the fad phenomenon and the usefulness of LLMs at the same time, because both are true.
There's value there, but there's also a lot of hype that will pass, just like the AGI nonsense that companies were promising their current/next model will reach.
Re: Reflections on AI at the End of 2025
#188Earlier quoted context omitted.
I thought I'd read a lot of these threads this year, and also discussed off-site the use of coding agents and the technology behind them; but this is genuinely the first time I've seen the term "RLVR".
RLVR "reinforcement learning for verifiable rewards" refers to RL used to encourage reasoning towards achieving long-horizon goals in areas such as math and programming, where the correctness/desirability of a generated response (or perhaps an individual reasoning step) can be verified in some way. For example generated code can be verified by compiling and running it, or math results verified by comparing to known c…
> The difficulty of using RL more generally to promote reasoning is that in the general case it's hard to define correctness and therefore quantify a reward for the RL training to use.
Ah, hence the "HF" angle.
Re: Reflections on AI at the End of 2025
#189Earlier quoted context omitted.
> The change occurred because LLMs are useful for programming in 2025 But the skeptics and anti-AI commenters are almost as active as ever, even as we enter 2026. The debate about the usefulness of LLMs has grown into almost another culture war topic. I still see a constant stream of anti-AI comments on HN and every other social platform from people who believe the tools are useless, the output is always unusable, pe…
> from people who believe the tools are useless, the output is always unusable, people who mock any idea that operator skill has an impact on LLM output You are attacking a strawman. Almost nobody claims that LLMs are useless or you can never use their output.
Those claims are all throughout this thread and in replies to my comments.
It’s not a strawman. It’s everywhere on HN.
Re: Reflections on AI at the End of 2025
#190> There are certain tasks, like improving a given program for speed, for instance, where in theory the model can continue to make progress with a very clear reward signal for a very long time. Super skeptical of this claim. Yes, if I have some toy poorly optimized python example or maybe a sorting algorithm in ASM, but this won’t work in any non-trivial case. My intuition is that the LLM will spin its wheels at a loc…
> but this won’t work in any non-trivial case Earlier this year google shared that one of their projects (I think it was alphaevolve) found an optimisation in their stack that sped up their real world training runs by 1%. As we're talking about google here, we can be pretty sure it wasn't some trivial python trick that they missed. Anyhow, at ~100M$ / training run, that's a 1M$ save right there. Each and every time t…
The second example has nothing to do with the first. I am optimistic that LLMs are great for translations with good testing frameworks.
“Optimize” in a vacuum is a tarpit for an LLM agent today, in my view. The Google case is interesting but 1% while significant at Google scale doesn’t move the needle much in terms of statistical significance. It would be more interesting to see the exact operation and the speed up achieved relative to the prior version. But it’s data contrary to my view for sure. The cynic also notes that Google is in the LLM hype game now, too.