recitedropper

gisterendiscuss

I agree with you--but just fyi I think "antifragile" is generally used in the opposite to what you mean. If I'm remember correctly Taleb has tried to coin it as a precise word to describe the inverse of your phenomena: Systems that prioritize robustness over optimizations, and therefore can handle stress effectively.

recitedropper

5 dagen geledendiscuss

Tell me you don't understand Taleb without telling me you don't understand Taleb.

recitedropper

vorige maanddiscuss

The cult in this case is TESCREAL, not everyone working on AI. Last I checked not all the "several thousand skilled workers" in AI subscribe to TESCREAL ideology, although it has been a while since I've been to the Bay. Maybe things have changed since my time at Berkeley, and Dario's belief that he will eventually be made immortal by mind uploading is more widespread.

Otherwise we agree that benchmarking is hard, the benchmarks contain hard problems, and that there are many hard working people trying to accurately gauge what is going on. It is getting harder to watch though as all that is on the line taints the overall endeavor.

recitedropper

vorige maanddiscuss

Do you see the pattern as new accounts tending to boost or criticis $LLM_PROVIDER? I think I see both...

Either way, I agree that HN is quickly becoming more manipulated and low SNR, like the rest of the entire internet.

recitedropper

vorige maanddiscuss

"Carefully and thoughtfully" is antithetical to the approach to benchmarks these days.

Maybe back when this was a scientific endeavor; not now when enormous, enormous amounts of capital are on the line. Along with an entire cult's chosen eschatology.

recitedropper

vorige maanddiscuss

I am having a similar sentiment change about our industry as well. The more AI's marketing plays purely on fear and shame, the more I want to see it fail. If Anthropic, OpenAI, and the other power players continue in this direction, I hope the graduation speech boos are just the start.

recitedropper

vorige maanddiscuss

Interesting: New account, made approximately 20 minutes after this was posted, to solely call this out as slop. Someone either hates Anthropic, or something fishy is going on here.

Honestly I'm pretty tired of Anthropic's press releases too, but this one is pretty benign. If I was a hater, I'd save up my new-account-energy for their next "paper" that insinuates Claude might be actively introspecting.

recitedropper

2 maanden geledendiscuss

Completely agreed. It looks like there is a concerted effort to "massage" opinion away from any substantial questioning of the ethics, companies, and people behind the AI push. Some of this inevitabilism is organic of course, but there is too much for it all to be so.

HN is way too central for shared sentiment in the tech world for these companies not to do some amount of astroturfing. AI companies have shown at every single turn that they act out of self-interest and greed, not of moral principles. So it isn't surprising, even if it is still sad, to see those who are commanding the most capital in human history act with such callousness.

I think the appropriate course of response is to stop adding to public spaces on the internet. No doubt painful for those of us who have so benefitted from the freely shared thoughts of others. But if well-funded bullies are going come in, steal everything, ruin the commons, and then say "this is the new normal, deal with it", there isn't much the rest of us can do other than stop feeding them.

recitedropper

2 maanden geledendiscuss

The opening line of my parent comment is: "This is impressive, no question." I am impressed, and the chain you are replying to is questioning how much that impression should be tempered.

They pay people for expert training data they do not share because it gives them an edge over other AI companies. And, as always, deep learning is enormously data-hungry, and we've gotten to the point where publicly available data has been exhausted.

AI companies absolutely retrain models regularly to keep up with the cutting edge. There is a reason why this announcement references an internal, unreleased model, rather than "we just put a lot of new math papers within the GPT5.5 context window and found this."

recitedropper

2 maanden geledendiscuss

  - https://www.theverge.com/cs/features/831818/ai-mercor-handshake-scale-surge-staffing-companies
  - https://outlier.ai/math/en-us
  - https://www.opentrain.ai/
  - https://www.pin.com/blog/ai-labs-hiring-train-models/

Much of this is data annotation, reasoning trace evaluation, and problem set curation. But there is no way they haven't atleast paid some mathematicians to work on research grade problems in tandem with their models, and then used that for training data.

Does this expert data likely contain this proof within it? No. Would it temper the impressiveness to know they have a large amount of novel mathematical training data, an internal Lean harness for evaluation of open conjectures, and spent hundreds of millions in compute to calculate this? Yes.

recitedropper

2 maanden geledendiscuss

It is interesting to me how controversial this post is. It has the highest upvotes, and most disagreeing comments, of anything I've typed up on HN.

I'll gladly admit I think what these companies are doing is unethical, and I'm sure that biases my thinking toward skepticism.

That said, there remains way too much that is hidden to be able to effectively evaluate what is going on. You have the perfect storm:

  - AI companies do not share their custom internal harnesses.
  - AI companies do not share their custom internal training data. 
  - AI companies do not share how much compute they allocate to trying to solve problems of this nature. 
  - AI companies are primarily marketing their models to investors as human-replacing rather than human-augmenting. 
  - AI companies are under enormous financial pressure to make their business work.

The last two points incentivize them to find these types of "first proof" successes as aggressively as they can, and I'm sure they've thrown the whole book at it.

Is it likely that they literally had a mathematician discover this, put it into the training data, and then prompted it out? Of course not.

But it would make a world of difference--in evaluating the impressiveness of this discovery and LLM capabilities in general--if we were to know the extent to which the training data crosses over this problem, the harness with which this was ran, and how much compute was spent.

Until they bring more transparency to the whole process--something which some of the mathematicians commenting on this even asked for--I will personally take discoveries of this nature with a good dose of salt.

recitedropper

2 maanden geledendiscuss

Thank you for engaging with my comment in a kind and authentic way.

recitedropper

2 maanden geledendiscuss

I'm not letting the government read my brainwaves.

In all seriousness though: My suggestion is that those shepherding the frontier of AI start acting with more transparency, and stop acting in ways that encourage conspiratorial thinking. Especially if the technology is as powerful as they market it as.

recitedropper

2 maanden geledendiscuss

This is impressive, no question.

Without knowing all this model has been trained on though, it is pretty hard to ascertain the extent to which it arrived to this "on its own". The entire AI industry has been (not so secretly) paying a lot of experts in many fields to generate large amounts of novel training data. Novel training data that isn't found anywhere else--they hoard it--and which could actually contain original ideas.

It isn't likely that someone solved this and then just put it in the training data, although I honestly wouldn't put that past OpenAI. More interesting though is the extent to which they've generated training data that may have touched on most or all of the "original" tenets found in this proof.

We can't know, of course. But until these things are built in a non-clandestine manner, this question will always remain.

recitedropper

2 maanden geledendiscuss

HN is enormously influential for programmers and employees within the tech industry. Who happen to be exactly who Anthropic, and other AI companies, desperately need adoption from...

recitedropper

2 maanden geledendiscuss

This is such an excellent summary of everything wrong with Silicon Valley's current ethos.

recitedropper

2 maanden geledendiscuss

My understanding is that the shortage has more to do with DRAM manufacturer capacity, rather than specifically making chips with high RAM amounts.

From TrendForce's analysis:

"The laptop market's 2026 shipments have been revised down from the previously expected annual growth of 1.7% to -2.8%, and further adjusted to -5.4%. Brands with highly integrated supply chains and more flexible pricing, such as Apple and Lenovo, have more flexibility to handle rising memory prices. However, low-end and consumer laptop brands face difficulty passing on costs and are constrained by processor and operating system requirements, making further spec reductions difficult."

Google can obviously just make this machine more expensive, but to launch a completely new brand of consumer laptops in a year where production is already very constrained is only going to exacerbate the core issue.

recitedropper

2 maanden geledendiscuss

Can't imagine this'll help the RAM shortage.

recitedropper

2 maanden geledendiscuss

That is what I had heard.

Checking now: The way they describe it in their FAQ is that if the price changes, then they will bill you the new price. But I read that as regarding if the primary model provider changes their headline token cost; not in the case of pricing differences for models that have many different backends that host them.

Regardless, I would be more concerned about the streaming costs if the service continues to blow up and they scale aggressively through VC investments. If their 5.5% skim accounted for what they needed, you'd think they could effectively grow organically..

recitedropper

2 maanden geledendiscuss

Streaming, caching, and tool calling can get pretty expensive with scale, even when you don't touch inference. Maybe they're doing something clever and are quite profitable.. or maybe they've already taken $40mm from VCs and are currently trying to raise $120mm at a 1.3B evaluation.

They also show headline prices for the cheapest provider of whatever model, but then need to hit different backends some of which may be more expensive. For now they absorb those costs, but the VCs always come knocking.

Just my opinion though. Totally agreed that they have one of the best positions amongst all AI providers from a financial standpoint.