bonplan23

I don't want to crash the party, but there is also a non AI-bubble-is-about-to-burst way to read this: If producing something becomes cheaper (e.g. more efficient inference techniques, cheaper compute etc.) and there is some competition, then typically prices go down as well.

After AI more or less “solved” large parts of mathematics overnight and literally broke the internet by uncovering decades-old PHP vulnerabilities, you still think this is a hype that’s cooling off?

To me, this mostly shows how much our baseline for what counts as “noteworthy” has shifted since ChatGPT came out. I’ve seen more disruptive events in the last couple of years than in the 30 before that.

What would it take to get you excited again? AI discovering that aliens exist?

Also the concern that open weights models could contain dormant adversarial behavior (i.e. they are sleeper agents) is more popular amongst decision makers than one would think.

In that regard it was a clever marketing move by Anthropic to release their paper "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training" ....

I saw the same kind of comments when the first Deepseek model was initially released. However the relevance that investors assign to something like this does not seem to match the excitement expressed here.

I think they do not believe that big corporations in the US or Europe would actually use chinese models in their business operations due to security, regulatory and/or business continuity concerns.

>just don't want to do that....

Nobody ever said that life is fair. Whether you still persist and find happiness typically depends on your willingness to adapt and evolve.

This kind of job is one in a million. In all other jobs you are expected to work in a team and if the interviewer thinks that you're lacking the basic interface for that - i.e. being able to articulate your thoughts in a concise way and not being annoying to work with - they just cannot hire you.

There are a lot of PhDs in my company and basically none of them was hired because of the specific topic* of research they were pursuing during their thesis. Yet having the PhD was a huge plus during hiring and in most cases even an (informal) requirement.

I think it happens quite rarely that a company is looking for a PhD based on the specifc topic of their thesis. If that ever happens, you can consider yourself lucky though, because they will probably pay you a fortnue if they really think that they couldn't put any other PhDs on that topic.

*that is not to say field of research like "Machine Learning", but rather something like "loss functions for graph neural networks".

Many of the comments here seem to stem from the implicit assumption that before claude, software products were thoughtfully designed, secure, reliable, and beautifully engineered works of art.

Typically something is "objectively" the case if there is indisputable evidence for it (of which you provided none).

... zero concerns about LLMs wiping out humanity. But world models based on JEPA totally will! Please come invest in my company ... :)

I think we will soon reach a point where code won't be considered secure unless AI has written or at least verified it. Company policies may then even consider that bad practice and/or forbid it.

If an entirely new problem, within a new field of mathematics were to appear tomorrow, I highly doubt a human mathematician would be useful at all on their own.

Just to be clear: It was well known that you can reach such scores with small models and without an LLM if you train on the task. The author highlights those models himself - e.g. HRM/TRM.

The novelty is more that it works with such a plain transformer and low compute price.