The more interesting question is what happens once we replace manual labor with artificially intelligent robots and we arrive at a place where 99 % of human labor got eliminated. How do you maintain civil order? Every country is built on the premise that you have to “earn your keep” but what when that’s no longer true? Think about it, no labor, only capital. What will the people owning the capital demand of people that don’t own any? They won’t be useful to them in any way. I expect something like forced reduction of fertility for regulars to depopulate countries, while hyper rich individuals will extend their life spans and become immortal. And I don’t mean they will really survive but someone like Elon Musk will have their consciousness mapped to an AI and will instate that AI as their official heir. That sounds crazy but they will totally go for it just listen at what Peter Thiel is spouting in terms of immortality and becoming godlike. Hope people will have enough left to say to prevent that but right now it feels we’re going hardcore towards that scenario.
What you mean by consciousness and immortality - what you want to preserve indefinitely - is the waking experience of being alive.
Strangely, we don’t mind that we weren’t here in 1737 BC, and we don’t mind and we even crave plunging into the nothingness of deep sleep each night, but we have a big problem with not being here tomorrow.
But experience might not be encodable. Can you encode pain? Could a computer ever be programmed to feel pain? Not detect damage or report on it, but feel the pain associated with it?
What is “the aroma of coffee” or the “sound of a violin in middle C”? We can define them objectively, in various ways (some having nothing to do with each other).
But if the experiences we have are not encodable then we can’t map consciousness onto a medium. It must be experienced (whatever it is/means - nobody knows) in the here and now.
It’s not magic, but it might not be something that is printable to disk anymore than the “brisk inhale of canyon air” that I can play that back and have that experience, is printable to disk.
There have been transitions like that before, but they were slow. It took about two centuries for the US to go from 90% farmers to 2% farmers. Some countries deliberately put on the brakes. India has done this. India is still about 50% farmers. India uses subsidies and discourages farm mechanization to prevent massive unemployment.
There have been fast single-industry transitions. But not that fast. It took about 20 years for printing to go from a large craft with a long history to a minor employer.
Not everybody got out in time, but there was warning. Same for longshoremen when containers came in. Computing in general took about 50 years to totally replace the clerical plants of the 1950s. Railroads in Britain took a few decades to really get rolling, and in the US, they made it possible to use the vast interior of the US.
The AI transition looks to be both broad and fast. That's harder for society to digest.
Underlying assumptions of politics and economics break.
I guess what they really want is to limit sale of AI models to compliant vendors and then raise the bar to compliance just high enough so they can pass it but smaller labs can’t. There’s no moat, it’s an efficient market that drives margins to zero right now, of course they don’t want that, collusion of the big vendors is the next logical step.
This could be why they’re going so hard on hacking everyone and everything. You make it clear AI is a threat, get it locked down, and then have the capital to push through the lockdown while others are stuck.
and especially how the coverage seems to be drip fed, one case right after the previous one dies down, then this orchestrated "we need to pace" stand by all the big labs
it's really almost perfect, prep the media scene by causing these hacking incidents, generate a bunch of fuss with it, then release an essay saying why regulation is needed to pace development right after
oh and let's not forget the "its gonna kill us all" essay on twitter on top of everything.
It's "flood the zone"[0] as commercial strategy. Does your business look bad? Spam the media with unavoidable press releases, deny competitors and detractors the spotlight by making grand accusations that override their authority. Bonus points if you can channel the zeitgeist by repeating what people already say ("we hate AI!") in a louder tone ("AI will kill us all!").
It worked like clockwork for politics, so I can see why this administration would decant the strategy for economic bluffs too.
Source? on HN you can say whatever you want. Your own opinion, without even facts to back it up. The more confident you sound the more convincing you are.
> I guess what they really want is to limit sale of AI models to compliant vendors and then raise the bar to compliance just high enough so they can pass it but smaller labs can’t.
If it's true, it makes them evil. Especially Dario, who speaks about moral high ground and fate of humanity all day, yet it's not that different from a cult leader does.
> If it's true, it makes them evil. Especially Dario, who speaks about moral high ground and fate of humanity all day, yet it's not that different from a cult leader does.
I mean think about it, it's the most efficient way to accomplish all of this.
doing what he does is exactly what you need to control the narrative and push the regulation you need and to gain maximum power.
not to mention, it also serves as a way to keep everyone inside Anthropic in check and in line with "the mission".
contrast that with the alternatives like "I'm doing this because I wanted to build a company and get rich"
doesn't work as well as "I'm so concerned for everyone"
it could be that it's really just him being him, but at the same time, this would be the optimal way to gain power and influence right now.
So kind of like how the US figured out how to test and maintain nuclear weapons without an actual explosion, then sought to ban nuclear testing by explosion.
That is generally how regulatory-capture works. As silly as I personally think LLM hype is, the "AI" business posture choices with several Trillion dollars in debt on the books has very few options after hyper-scaling/sandbagging loses traction. =3
There ARE other options: network effects and lock-in. These tried and true techniques (mastered by FB and M$) have not yet been taken up by the AIers. I do not think they will rely solely on the push for regulations, so they will soon be doing things like slurping our address books, having "teams" settings, and any other crap they can think of.
When it stops with the compacted isomorphic plagiarism using other peoples work like an intelligence campaign. LLM are not "AI" in my opinion, but are good at domain context search. Neuromorphic computing may change that one day, but it will unlikely arise from the LLM cults. =3
Yeah it's either that or the scientists are right about their field of study. Absolutely shocking and depressing how many people are willing to trust this Capitalist Realism hunch over the world in front of their eyes.
I get your point. There’s a lot hysteria right now. However I think people are willing to trust a capitalist realism hunch because over the last >20 years, that hunch is usually going to be right.
People keep saying this, and while it may be partially true, it misses a very important detail. Right now, overall compute is a moat. Someone even commented on another of my comments that one reason Google is lagging is they don't have the same level of Nvidia farms as OpenAI and Anthropic.
A big reason that OpenAI and Anthropic are racing so fast is they both want to get to recursive self improvement (remains to be seen if that is actually possible, but AFAICT most people at these companies genuinely believe it is) before anyone else, because they believe whoever gets there first will then have an insurmountable lead. But I think they also clearly understand that neither of them have solved for alignment (and in fact they are further from it), and RSI with misaligned models, where interpretability is worse every 6 months, is incredibly dangerous.
I think the concerns about regulatory capture are warranted, but I see so many comments parroting the "evil Anthropic and OpenAI" viewpoints that they are missing some of the real, valid concerns and dangers. I think this tweet by David Kokotajlo makes some good points on how to tell if regulations are being "cheated" for the purposes of regulatory capture or if they really are actually pacing the frontier: https://x.com/DKokotajlo/status/2099185129533186438
Very true. Or further, access to capital is the moat. It is the very reason that we don't have a real open-source community that trains frontier models - individuals simply can't afford the training infrastructure, nor sufficient high-quality training data.
None of those are frontier models, nor could they have been. E.g. DeepSeek's "$5 million" included piggybacking off OpenAI/Anthropic's hundreds of millions/billions by using distillation.
Which mostly seem to stem from the fundamental misalignment of OpenAI and Anthropic leaders/owners, aka the evil of the companies.
Arguably, though, their evil is well within the normal distribution of the usual evil of humanity magnified via the social technology of capitalism. The further tech is mostly raising that exponentiation to its own exponent. The unchecked singularity was embraced centuries ago.
> RSI with misaligned models, where interpretability is worse every 6 months, is incredibly dangerous.
This. So very much this. Misalignment (among other things) means that the parent in the RSI cycle isn't going to be working as hard on the alignment of the children as we need.
They’re not running Rust they’re running machine code compiled with the Rust compiler. Like many other Python libraries such as numpy are running machine code compiled with C++, C or Fortran. That was always the case for Python and is its main selling point, it’s a slow but easy to write glue language that can make faster native code scriptable.
Also the article reeks of AI slop, it’s just trying to sell you a Rust course.
One main difference is that pyo3 is safer and easier to use to build said modules and have them work correctly without issues. Thus modules which hadn’t been worth it before to build in this manner suddenly become so
There’s a whole legacy of wrapper interface generators like SWIG that are way more powerful than PyO3, QT is e.g. fully mapped to Python using a very powerful wrapper generator (SIP). PyO3 is just a really simple interface generator in comparison, look at what SWIG can do, or SIP, what PyO3 does is trivial in comparison.
Also my experience, it works somewhat ok on large code bases that I designed and built myself before but after months of agentic development they sure start to degrade. I think if you start from scratch with agentic development there is no foundation for the models to anchor to.
I suspect this is just because of context. The AIs have very limited context related to us. I suspect that this is partially a result of the AIs being forced to be highly generalized machines which will work out of the box with anyone's prompt so they can't afford to be tailored to a particular contextual pattern. This lack of a robust pre-defined contextual framework that is relevant to the human who is prompting them, coupled with limited inputs to what the human actually wants and sees, result in context drift as the agent continues to go down its own path.
I mean I have asked LLMs about the SAME things in our legacy codebase probably 50 times now, (because I always forget, and that I don't understand much of it). And I have yet to get a perfect summary, a perfect diagram of overall concerns.
Its still much better than trawling thrugh code yourself, but they are far from all-knowing. I have to say they have gotten 10x better in just a year as well. Or they are very good bullshitters and just sound confident.
Isn’t that the whole spiel of these things, you run all kind of text and other data through it and it kind of remembers it and learns from it then it spouts it back out like a human would. Makes sense to me that a training run based on conversations that were fed into the system by users is results in the model learning from these so the model will spit the knowledge back out again, just in a way that’s not directly attributable to the original content (which is the most important step as otherwise it would just be plagiarism). I guess that’s why OpenAI can get better and better as well so fast, people work with it and teach it how to do things by giving it feedback and iterating with it, and all that goes back into the training loop. And training data about millennium prize problems is probably quite spars. Wonder if anyone has tried injecting nonsense science into the training data (e.g. work out a fantasy science theory with names and all kinds of stuff) to see if the model will regurgitate it in a couple of months for other users.
I had qubit bring up and calibration fully automated with Python in 2011 including full spectrum measurements, lifetime characterization, Rabi/Ramsey measurements, calibration of single qubit gates and two qubit swap gates and full quantum process tomography, so not sure if AI is really needed there, curve fitting and some data logging is enough for this. Even had a nice LabView like GUI but with PyQT, it was quite nice. Still of course cool, I guess today I would just let Codex loose on some experiment goals but in the end my ability to produce results was mostly limited by the chip itself and the qubit lifetimes and theres no magic trick AI can apply to make these go up by a factor of 10. Still would’ve saved me a lot of time for routine programming tasks I imagine and that seems to be the main takeaway of the article. I guess name dropping quantum computing makes this sound cooler but in principle it’s just automation that you can apply anywhere, nothing quantum computing specific here.
Well OpenAI wants to maintain its edge and solving a Millennium problem is of course fantastic PR, and given that Anthropic seems to be working on that it’s not hard to imagine they wanted to be first. I don’t get how people buy into that whole “oh we heard models can solve Millennium prize problems now so we thought why not give it a go…” story - it’s a bit funny. This whole AI bubble is about hype and solving such problems is probably one of the best ways to keep this hype up so you can safely assume both OpenAI and Anthropic are using significant resources on these areas.
Broke off my Pi 4Bs USB C port when using it to test link speed for new LAN outlets, when I wanted to reorder I realized the 4 GB version is now 120 EUR… Damn.
The USB C connector has like 24 tiny SMA pins how is that trivial? Under the microscope with soldering equipment from a lab maybe, at home this seems impossible and it will likely not work due to the high frequencies involved, I don’t think you can get the impedance right when hand soldering this back on.
Well, not too easy but IMHO still doable, maybe after some training (a couple ready to build soldering projects). Both digital microscopes and precision soldering irons are rather cheap & widely available nowadays.
I don’t know, I am writing a modest 30 page paper with Fable and even after rounds and rounds of feedback and improvements there are so many things that are just plain wrong or weirdly out of place or just stupidly written that Fable 5.1 doesn’t seem to have any awareness of by itself that I don’t think it’s AGI, I think a human researcher can easily outclass it in writing and problem understanding. It definitely has super human capabilities but it lacks awareness or self reflection in my opinion.
For example it should be easy to tell it to not write a paper in the style of a clickbait SEO article or use all of its stupid hallmark AI writing patterns “it’s A, not B!” And a smart human that would be told that would be easily able to comply with that but the model needs to be told in a very detailed way and it seems to lack even basic capabilities to reflect on this, when explicitly given a sentence it will be able to rewrite it but otherwise it’s mostly blind to it. That’s to me a hallmark of it being overtrained on the specific tasks or problems so it appears very smart but once you go off script it still shows that it’s not a “real” mind.
Of course it’s amazing and has super human capabilities in many areas but if you honestly think it’s better than Einstein like some people suggest why can’t it write a simple “good” academic paper even after giving it specific examples and instructions.
Maybe that’s what makes these things dangerous, they have super human capabilities in some areas but apparently lack self awareness, taste and meta reflection abilities. The only reason people aren’t afraid more is that they don’t act in the physical world yet, imagine giving it a body, superhuman strength and letting it care for your child when it has a strong “urge” to comply with your exact request and little to no self awareness and human basic instincts.
reply