samuelknight

Same with the consumer variants. RTX 3090 launched at $1499 and used ones on ebay are over $1500. GPUs large enough and new enough to do inference are not losing value.

Volta generation prices are collapsed, so GPU depreciation is currently around 8 or 9 years

Hey, my startup is a platform for automated penetration testing (so white hat hacking of your own infrastructure). There are several straightforward ways to track CVEs, and you can also automate parts of the patching process (such as with dependabot, since you mentioned Github advisories). Those can inform you of specific bugs in common software but doesn't tell you much about your security posture. For example your in house software could be buggy, or your environments could be mis-configured. At this point CVEs are reactionary. Everyone (including criminals) are finding exploits to common software at about the same time which means patches come weeks after the bugs start getting exploited in the wild.

A criminal who thinks you have something worth stealing will set up an agent to attack your systems. The bottom line is that a cheap LLM in 2026 is far more capable than than the best unassisted human hacker. CVEs put both the attacker and defender on notice, but they are are a tiny part of what goes into making a well defended system.

On the contrary, demand compounds with every incremental improvement of model capability. There was no price that justified using LLAMA 3 70B for today's agentic use cases. That said, there is a price where it doesn't make sense to buy the next GPU.

I can appreciate the complexity argument, but that assumes that AIs can't standardize common patterns into pathological cases. For example our AIs today can code slop, but the slop doesn't try to re-create the IP stack. Why does the author assume we can't make AIs that optimize for better long term design view much like they can optimize for easier goals today like code throughput or program speed?

Look at the citations on the graph, the agents are trying off the wall ideas like 30 dimensional geometry and E8. If any of these ideas are particularly fruitful, humans can retroactively use their very limited attention and build it into a mathematical cannon. The take away from this is that we getting to see an early instance of a distributed agent swarm relentlessly work a hard problem with progress that can be tracked by a simple metric (maximize kappa).

You can tell by the shape of the board that it's meant to go into a server chassis behind a stack of 80mm blower fans. It's not meant for consumers, but you will be able to buy it used for $200 off ebay in 7 years to put in your home lab.

They can hire more people and still be massively augmented by AI.

The DFT paper is an example against AI 'strip-mining'. The paper introduces a new method, and researchers are already trying to improve on it. If anything, the OpenAI dump re-vitalized that branch of study.

It is not artificial constraint. The build out is titanic; the supply of AI compute is doubling every 6.8 months. To feed that much growth hyperscalers have to price everyone else out of the market.

Slide 12 and 13 sum up my conclusions from the Oct 8 100+ reactions to 100+ solutions. Several reported OpenAI drop included answers put a wrap on problems they had been working for years. Several said that they have to completely rewrite grant requests that they had just submitted. Others described learning of the solutions problems they had been working on like losing an old friend or a lover. The general sentiment was to bemoan the loss of a field, as if it would have been better be born in the early 20th century and conclude their career arc before reaching this point. My take away is that the field needs to get it through their heads that their old problems are no longer ambitious, and that their job in the short term is to find the new frontier.

One idea Terrance Tao conjectures, which is highly doubtful, is that spamming the AI button will solve open problems without producing insightful new methods. But the OpenAI drop would seem to disprove this. The sub O(nlogn) proof for DFT for example violated very old human assumptions. Decades of work in the field was incremental progress on sub optimal method that nobody questioned hard enough. More generally, we should always be able to go back to a super-human AI and say, "Attack this problem, but don't use a method tried before."

The first part of the article is a thoughtful framework. The conclusion that LLMs are not inevitable because they aren't useful or that their utility can be substituted by humans is an extraordinarily flimsy claim.

The mathematician in this case didn't ask an AI for the proof, they were handed a badly written artifact and other people are messaging him asking for analysis. He has no visibility into the process that produced the paper because it came from an internal OpenAI model. The bad behavior here isn't coming from the complaining mathematician. OpenAI hasn't sufficiently developed their workflow to create cutting edge research AND publish it intelligibly, which is odd because the latter should be easier. OpenAI has perverse incentive to do this because spamming badly written papers can still give them priority credit for solving open problems at the expense of mathematicians (who are currently indispensable because they decide whether claims are credible). What Mr. Karagila is trying to enforce are the old standards that review should come after refined explanation of the solution. If that was the case before AI, I can't see a reality in which that isn't the standard now that papers can be pumped out at faster-than-human time scales.