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amluto
I've contemplated this a bit, and I think I have a bit of an unconventional take:
First, this is really impressive.
Second, with that out of the way, these models are not playing the same game as the human contestants, in at least two major regards. First, and quite obviously, they have massive amounts of compute power, which is kind of like giving a human team a week instead of five hours. But the models that are competing have absolutely massive memorization capacity, whereas the teams are allowed to bring a 25-page PDF with them and they need to manually transcribe anything from that PDF that they actually want to use in a submission.
I think that, if you gave me the ability to search the pre-contest Internet and a week to prepare my submissions, I would be kind of embarrassed if I didn't get gold, and I'd find the contest to be rather less interesting than I would find the real thing.
asboans
Firstly, automobiles are really impressive.
Second, with that out the way, these cars are not playing the same game as horses… first, and quite obviously they have massive amounts of horsepower, which is kind of like giving a team of horses… many more horses. But also cars have an absolutely massive fuel capacity. Petrol is such an efficient store of chemical energy compared to hay and cars can store gallons of it.
I think if you give my horse the ability of 300 horses and fed it pure gasoline, I would be kind of embarrassed if it wasn’t able to win a horse race.
furyofantares
Yeah man, and it would be wild to publish an article titled "Ford Mustang and Honda Civic win gold in the 100 meter dash at the Olympics" if what happened was the companies drove their cars 100 meters and tweeted that they did it faster than the Olympians had run.
Actually that's too generous, because the humans are given a time limit in ICPC, and there's no clear mapping to say how the LLM's compute should be limited to make a comparison.
It IS an interesting result to see how models can do on these tests - and it's also a garbage headline.
krisoft
> what happened was the companies drove their cars 100 meters and tweeted that they did it faster than the Olympians had run
That would be indeed an interesting race around the time cars were invented. Today that would be silly, since everyone knows what cars are capable of, but back then one can imagine a lot more skepticism.
Just as there is a ton of skepticism today of what LLMs can achieve. A competition like this clearly demonstrates where the tech is, and what is possible.
> there's no clear mapping to say how the LLM's compute should be limited to make a comparison
There is a very clear mapping of course. You give the same wall clock time to the computer you gave to the humans.
Because what it is showing is that the computer can do the same thing a human can under the same conditions. With your analogy here they are showing that there is such a thing as a car and it can travel 100 meters.
Once it is a foregone conclusion that an LLM can solve the ICPC problems and that question has been sufficiently driven home to everyone who cares we can ask further ones. Like “how much faster can it solve the problems compared to the best humans” or “how much energy it consumes while solving them”? It sounds like you went beyond the first question and already asking these follow up questions.
in-silico
Cars going faster than humans or horses isn't very interesting these days, but it was 100+ years ago when cars were first coming on the scene.
We are at that point now with AI, so a more fitting headline analogy would be "In a world first, automobile finishes with gold-winning time in horse race".
Headlines like those were a sign that cars would eventually replace horses in most use-cases, so the fact that we could be in the the same place now with AI and humans is a big deal.
hnfong
All the while with skeptics snarkily commenting "Cars can move fast, but they can't really run like a human!"
LPisGood
I think your analogy is interesting but it falls apart because “moving fast” is not something we consider uniquely human, but “solving hard abstract problems” is
apstls
This metaphor drops some pretty key definitional context. If the common belief prior to this race was that cars could not beat horses, maybe someday but not today, then the article is completely reasonable, even warranted.
Swizec
> Firstly, automobiles are really impressive. Second, with that out the way, these cars are not playing the same game as horses
Yes. That’s why cars don’t compete in equestrian events and horses don’t go to F1 races.
This non-controversial surely? You want different events for humans, humans + computers, and just computers.
Notice that self driving cars have separate race events from both horses and human-driven cars.
in-silico
The point is that up until now, humans were the best at these competitions, just like horses were the best at racing up until cars came around.
The other commenter is pointing out how ridiculous it would be for someone to downplay the performance of cars because they did it differently from horses. It doesn't matter if they did it using different methods, that fact that the final outcome was better had world-changing ramifications.
The same applies here. Downplaying AI because it has different strengths or plays by different rules is foolish, because that doesn't matter in the real world. People will choose the option that that leads to the better/faster/cheaper outcome, and that option is quickly becoming AI instead of humans - just like cars quickly became the preferred option over horses. And that is crazy to think about.
gxs
Yeah I think the only thing OP was passing judgement on is on the competition aspect of it, not the actual achievement of any human or non human participant
That’s how I read it at least - exactly how you put it
throw310822
I think you missed that the whole point of this race was:
"did we build a vehicle faster than a horse, yes/no?"
Which matters a lot when horses are the fastest land vehicle available. (We're so used to thinking of horses as a quaint and slow mean of transport that maybe we don't realize that for millennia they've been the fastest possible way to get from one place to another.)
lbrandy
I was struck how the argument is also isomorphic to how we talked about computers and chess. We're at the stage where we are arguing the computer isn't _really_ understanding chess, though. It's just doing huge amounts of dumb computation with huge amounts of opening book and end tables and no real understanding, strategy or sense of whats going on.
Even though all the criticism were, in a sense, valid, in the end none of it amounted to a serious challenge to getting good at the task at hand.
LaffertyDev
I don’t think you’ll find many race tracks that permit horses and cars to compete together.
(I did enjoy the sarcasm, though!)
j_timberlake
This response is good but the more general problem is that people are in "It doesn't look like anything to me" mode like Westworld robots seeing advanced technology. If there's a way to snap people out of that, I've never seen it.
GoatInGrey
Snark aside, I would expect a car partaking in a horse race to beat all of the horses. Not because it's a better horse, but because it's something else altogether.
Ergo, it's impressive with nuance. As the other commenter said.
rich_sasha
There's a difference. How much money went into training the computer here Vs the human? If you want to prove that a computer can, at extreme cost and effort, beat a human - sure, it's possible.
But you can also conclude that putting a lot of money and effort pays off. It's more like comparing a horse to a Ferrari that had millions of development costs, has a team of engineers maintaining it, isn't reusable, and just about beats Chestnut. It's a long way until the utility of both is matched.
melenaboija
Comparing power with reasoning does not make any sense at all.
Humans have surpassed their own strength since the invention of the lever thousands of years ago. Since then, it has been a matter of finding power sources millions of times greater such as nuclear energy
paladin314159
> I think that, if you gave me the ability to search the pre-contest Internet and a week to prepare my submissions, I would be kind of embarrassed if I didn't get gold, and I'd find the contest to be rather less interesting than I would find the real thing.
I don't know what your personal experience with competitive programming is, so your statement may be true for yourself, but I can confidently state that this is not true for the VAST majority of programmers and software engineers.
Much like trying to do IMO problems without tons of training/practice, the mid-to-hard problems in the ICPC are completely unapproachable to the average computer science student (who already has a better chance than the average software engineer) in the course of a week.
In the same way that LLMs have memorized tons of stuff, the top competitors capable of achieving a gold medal at the ICPC know algorithms, data structures, and how to pattern match them to problems to an extreme degree.
amluto
> I can confidently state that this is not true for the VAST majority of programmers and software engineers.
That may well be true. I think it's even more true in cases where the user is not a programmer by profession. I once watched someone present their graduate-level research in a different field and explain how they had solved a real-world problem in their field by writing a complicated computer program full of complicated heuristics to get it to run fast enough and thinking "hmm, I'm pretty sure that a standard algorithm from computer graphics could be adapted to directly solve your problem in O(n log n) time".
If users can get usable algorithms that approximately match the state of the art out of a chatbot (or a fancy "agent") without needing to know the magic words, then that would be amazing, regardless of whether those chatbots/agents ever become creative enough to actually advance the state of the art.
(I sometimes dream of an AI producing a piece of actual code that comes even close to state of the art for solving mixed-integer optimization problems. That's a whole field of wonderful computer science / math that is mostly usable via a couple of extraordinarily expensive closed-source offerings.)
avcxz
> That's a whole field of wonderful computer science / math that is mostly usable via a couple of extraordinarily expensive closed-source offerings.
Take a look at Google OR-Tools: https://developers.google.com/optimization/
bubblyworld
Yeah, absolutely, I spent months preparing for the ICPC with my team and ended up scoring a paltry 3/12. A week would likely not have helped us at all - we simply had no idea how to approach the rest! Top teams are on another level.
Compute and such is a fair point but that AI is here at all is mind-blowing to me.
dddgghhbbfblk
I think that's because the framing around this (and similar stories about eg IMO performances) is imo slightly wrong. It's not interesting that they can get a gold medal in the sense of trying to rank them against human competitors. As you say, the direct comparisons are, while not entirely meaningless, at least very hard to interpret in the best of cases. It's very much an apples to oranges situation.
Rather, the impressive thing is simply that an AI is capable of solving these problems at all. These are novel (ie not in training set) problems that are really hard and beyond the ability of most professional programmers. The "gold medal" part is informative more in the sense that it gives an indication of how many problems the AI was able to solve & how well it was able to do them.
When talking with some friends about chatgpt just a couple years ago I remember being very confident that there was no way this technology would be able to solve this kind of novel, very challenging reasoning problem, and that there was no way it would be able to solve IMO problems. It's remarkable how quickly I've been proven wrong.
ragequittah
It feels like half of the people I see talk about AI are still under the impression it's a spicy autocomplete. If you use a SOTA model for a week and still feel this way your bias must be very strong.
dddgghhbbfblk
I must be missing something because I don't understand how this is related to my comment.
modeless
It doesn't matter how many instances were running. All that matters is the wall clock time and the cost.
The fact that they don't disclose the cost is a clue that it's probably outrageous today. But costs are coming down fast. And hiring a team of these guys isn't exactly cheap either.
zeroonetwothree
Human teams are limited to three people. So why doesn’t it matter how many instances they used?
kenjackson
This is what the argument is? 10 years ago if you said you could do this with every computer on the planet and every computer scientist focused on trying to create the code to do this I would’ve given you absurd odds against it getting 12 problems right on ICPC. 10 years ago it couldn’t even reliably parse the question statement.
modeless
Human brains and cloud instances are not remotely equivalent. What you can compare on an equivalent basis is cost.
ben_w
All instances of any given model are kinda the same, for lack of a better word, "person": same knowledge, same skills, same failings.
warkdarrior
I bet with human teams it'll take longer to solve a problem the more people you have on the team.
tdb7893
As someone who has been to the ICPC finals around a decade ago I agree that the limited time is really the big problem that these machine learning models don't really experience in the same way. Though that being said these problems are hard, the actual coding of the algorithms is pretty easy (most of the questions use one of a handful of algorithms that you've implemented a hundred times by the time you're in the finals) but recognizing which one will actually solve the problem correctly is not obvious at all. I know a lot of people that struggled in their undergrad algorithms class and I think a lot of those people given the ICPC finals problems would struggle even with being able to research.
OtherShrezzing
The human teams also get limited to one computer shared between 3 people. The models have access to an effectively unbounded number of computers.
My argument does feel a bit like the “Watson doesn’t need to physically push the button” equivalents from when that system beat Jeopardy for the first time. I assume 5 hours on a single high-end Mac would probably still be enough compute in the near future.
Darmani
The people saying that were wrong, BTW -- Watson did have to physically press a button.
https://www.wired.com/2011/02/ibm-watson-speed/
> __Brown: __ Watson has a mechanical button-presser. It uses the same signaling device [the button] that the human competitors use in the game. Once Watson has decided that it wants to ring in because it has found an answer with a high-enough confidence, and it receives the signal that the buzzers are open and you can ring in, it then has to trigger the mechanical button presser and mechanically press the button.
OtherShrezzing
Watson originally just sent an electronic signal. The physical button pushing was introduced later, to stave off the criticism that it had an unfair advantage.
amluto
I found the Watson match to be rather absurd. It would have been much more interesting if the rules had been modified so that all contestants had, say, two seconds two press the buzzer and that the contestant who got to answer first would be chosen by random selection among those who pressed the button. This would at least have made the competition be about who could come up with the most correct answers (questions).
theragra
I think your analogy is lacking. Human brain is much more efficient, so it is not right to say "giving a human team a week instead of five hours". Most likely, the whole OpenAI compute cannot match one brain in terms of connections and relations and computation power.
stevenhuang
As always with these comparisons you neglect to account for the eons necessary for evolution to create human brains.
GoatInGrey
But as a product of evolved organisms, LLMs are also a product of evolution. They also came several hundreds of thousands of years later.
vrighter
so in that case, don't compare them to humans in the first place
_diyar
I think your assessment is spot on. But I also think there's a bigger picture that's getting lost in the sauce, not just in your comment but in the general discourse around AI progress:
- We're currently unlocking capabilities to solve many tasks which could previously only be solved by the top-1% of the experts in the field.
- Almost all of that progress is coming from large scale deep learning. Turns out transformers with autoregression + RL are mighty generalists (tho yet far from AGI).
Once it becomes cheap enough so the average joe can tinker with models of this scale, every engineering field can apply it to their niche interest. And ultimately nobody cares if you're playing by the same rules as humans outside of these competitions, they only care that you make them wealthy, healthy and comfy.
modeless
More information on OpenAI's result (which seems better than DeepMind's) from the X thread:
> our OpenAI reasoning system got a perfect score of 12/12
> For 11 of the 12 problems, the system’s first answer was correct. For the hardest problem, it succeeded on the 9th submission. Notably, the best human team achieved 11/12.
> We had both GPT-5 and an experimental reasoning model generating solutions, and the experimental reasoning model selecting which solutions to submit. GPT-5 answered 11 correctly, and the last (and most difficult problem) was solved by the experimental reasoning model.
I'm assuming that "GPT-5" here is a version with the same model weights but higher compute limits than even GPT-5 Pro, with many instances working in parallel, and some specific scaffolding and prompts. Still, extremely impressive to outperform the best human team. The stat I'd really like to see is how much money it would cost to get this result using their API (with a realistic cost for the "experimental reasoning model").
bazmattaz
Ha so true. I was so tempted to copy and paste a problem into GPT5 and see what it would say
HardCodedBias
They likely had a prompt that gave considerable guidance.
Hopefully that prompt was the same for all questions (I think that is what they did for the IMO submission, or maybe it was Google that did that, not sure).
qwertox
> it succeeded on the 9th submission
What's the judgement here? Was it within the allotted time, or just a "try as often as you need to"?
modeless
It was within the allotted time. If I'm reading the scoreboard correctly [edit: I wasn't], the human teams typically submitted dozens or hundreds of attempts at each problem.
kevinwang
For problems that human teams eventually get correct, they seem to have submitted mostly 1 time -- occasionally 2 or 3. For problems that they did not get correct, there are some problems with up to 16 submissions.
jojomodding
The way the rules work is that you can submit as often as you want. Team with the most solved problem wins. The time it took to solve all the problems is the tiebreaker.
But submitting a non-working solution gives you a time penalty (usually 20 mins). Yet this time penalty only applies if in the end, you actually solve the problem. So it never hurts to try.
JohnKemeny
I went to ICPC's web pages, downloaded the first problem (problem A) and gave it to GPT-5, asking it for code to solve it (stating it was a problem from a recent competitive programming contest).
It thought for 7m 53s and gave as reply
# placeholder
# (No solution provided)seanmmward
If you want to see some example solutions (not yet validated via official judging): https://github.com/iGentAI/icpc-maestro-solutions-2025
notemap
Don't trust it. Problem B required an algorithm to run for inputs up to N ~ 200, and a clever graph theory lemma, before succumbing to pattern matching / law of larger numbers. Claiming that there's a pattern for N < 20 seems like classic AI slop.
EDIT: Just submitted it, WA. Yeah.
Problems are hard enough where consumer models can't solve all 12 problems.
CamperBob2
Sounds like a bug. Did you try it again (or with another leading-edge model) and get a similar result?
patrickhogan1
1. What was your prompt? 2. Why did you give it to GPT-5 instead of GPT-5 Thinking or GPT-5 Pro?
patrickhogan1
Here is the prompt I just gave to GPT-5 Pro - its chugging on it. Not sure if it will succeed. Let's see what happens. I did think about converting the PDF to markdown, but figured this prompt is more fair.
-
You are a gold level math olympiad competitor participating in the ICPC 2025 Baku competition. You will be given a competitive programming problem to solve completely.
All problems are located at the following URL: https://worldfinals.icpc.global/problems/2025/finals/problem...
Here is the problem you need to solve and only solve this problem:
<problem> Problem B located on Page 3 of the PDF that starts with this text - but has other text so ensure you go to the PDF and look at all of page 3
To help her elementary school students understand the concept of prime factorization, Aisha has invented a game for them to play on the blackboard. The rules of the game are as follows.
The game is played by two players who alternate their moves. Initially, the integers from 1 to n are written on the blackboard. To start, the first player may choose any even number and circle it. On every subsequent move, the current player must choose a number that is either the circled number multiplied by some prime, or the circled number divided by some prime. That player then erases the circled number and circles the newly chosen number. When a player is unable to make a move, that player loses the game.
To help Aisha’s students, write a program that, given the integer n, decides whether it is better to move first or second, and if it is better to move first, figures out a winning first move.</problem>
Your task is to provide a complete solution that includes: 1. A thorough analysis and solution approach 2. Working code implementation 3. Unit test cases with random inputs 4. Performance optimization to run within 1 second
Use your scratchpad to think through the problem systematically before providing your final solution.
<scratchpad> Think through the following steps:
1. Problem Understanding: - What exactly is the problem asking for? - What are the input constraints and output requirements? - Are there any edge cases to consider?
2. Solution Strategy: - What algorithm or mathematical approach should be used? - What is the time complexity of your approach? - What is the space complexity? - Will this approach work within the given constraints?
3. Implementation Planning: - What data structures will you need? - How will you handle input/output? - What are the key functions or components?
4. Testing Strategy: - What types of test cases should you create? - How will you generate random inputs within the problem constraints? - What edge cases need specific testing?
5. Optimization Considerations: - Are there any bottlenecks in your initial approach? - Can you reduce time or space complexity? - Are there language-specific optimizations to apply? </scratchpad>
Now provide your complete solution with the following components:
<analysis> Provide a detailed analysis of the problem, including: - Problem interpretation and requirements - Chosen algorithm/approach and why - Time and space complexity analysis - Key insights or mathematical observations </analysis>
<solution> Provide your complete, working code solution. Make sure it: - Handles all input/output correctly - Implements your chosen algorithm efficiently - Includes proper error handling if needed - Is well-commented for clarity </solution>
<unit_tests> Create comprehensive unit test cases that: - Test normal cases with random inputs within constraints - Test edge cases (minimum/maximum values, boundary conditions) - Include at least 5-10 different test scenarios - Show expected outputs for each test case </unit_tests>
<optimization> Explain any optimizations you made or could make: - Performance improvements implemented - Memory usage optimizations - Language-specific optimizations - Verification that solution runs within 1 second for maximum constraints </optimization>
Take all the time you need to solve this problem thoroughly and correctly.
notemap
If we're benchmarking problems, mind trying out this problem on Pro if you're willing to spare the compute?
https://www.acmicpc.net/problem/33797
I have the 20$ plan and I think I found a weird bug, at least with the thinking version. It gets stuck in the same local minima super quickly, even though the "fake solution" is easily disproved on random tests.
It's at the point where sometimes I've fed it the editorial and it still converges to the fake solution.
https://chatgpt.com/share/68c8b2ef-c68c-8004-8006-595501929f...
I'm sure that the model is capable of solving it, but seriously I've tried across multiple generations (since about when o3 came out) to get GPT to solve this problem and it's not hampered by its innate ability I don't think, it literally just refuses to think critically about the problem. Maybe with better prompting it doesn't get stuck as hard?
birktj
They apparently managed gold in the IOI as well. A result that was extremely surprising for me and causes me to rethink a lot of assumptions I have about current LLMs. Unfortunately there was very little transparency on how they managed those results and the only source was a Twitter post. I want to know if there was any third party oversight, what kind of compute they used, how much power what kind of models and how they were set up? In this case I see that DeepMind at least has a blog post, but as far as I can see it does not answer any of my questions.
I think this is huge news, and I cannot imagine anything other than models with this capability having a massive impact all over the world. It causes me to be more worried than excited, it is very hard to tell what this will lead which is probably what makes it scary for me.
However with so little transparency from these companies and extreme financial pressure to perform well in these contests, I have to be quite sceptical of how truthful these results are. If true I think it is really remarkable, but I really want some more solid proof before I change my worldview.
XenophileJKO
So outside of human intervention, I don't think the specifics really matter. What this means is that it is possible and that this capability will in time be commoditized.
This is helpful in framing the conversation, especially with "skeptics" of what these models are capable of.
birktj
To a certain extent I agree. But as far as I know I cannot go to chatgpt.com and paste the newest ICPC problems and get full solutions. And there is no information about what they do differently. For a competition like the ICPC, which is academic in its nature, I think it is very unfortunate to setup a seperate AI track like this without publishing clear public information about what that actually entails. And have clear requirements for these AI companies to publish their methology. I know it is a nice source of sponsorships for them, but the ICPC should afford to stand up a bit for academic integrity.
Without any of this I can't even know for sure if there was any human intervention. I don't really think so, but as I mentioned the financial pressure to perform well is extreme so I can totally see that happening. Maybe ICPC did have some oversight, but please write a bit about it then.
If you assume no human intervention then all of this is of course irrelevant if you only care about the capabilities that exist. But still the implications of a general model performing at this level vs something more like a chess model trained specifically on competitive programming are of course different, even if the gap may close in the future. And how much compute/power was used, are we talking hundreds of kWhs? And does that just means larger models than normally or intelligent bruteforcing through a huge solutionspace? If so, then it is not clear how much they will be able to scale down the compute usage while keeping the performance at the same level
baq
If you assume the brain is a computer (why wouldn't it be is my stance), we have a long ways to go in the optimization department, both in hardware and in software. If it's possible to do at all using hundreds of kilowatt-hours of electricity, no reason it shouldn't be possible within a few hundred Wh (which is a scary prospect, I agree, with consequences hard to imagine when realized.)
GolfPopper
Mechanical Turking, in the original sense of the word.
conradkay
I don't see that much reason to be skeptical since this basically lines up with the trend we've been seeing in their performance.
ferguess_k
I think in the future information will be more walled -- because AI companies are not paying anyone for that piece of information, and I encourage everyone to put their knowledge on their own website, and for each page, put up a few urls that humans won't be able to find (but can still click if he knows where to find), but can be crawled by AI, which link to pages containing falsified information (such as, oh the information on url blah is actually incorrect, here you can find the correct version, with all those explanations, blah blah -- but of course page blah is the only correct version).
Essentially, we need to poison AI in all possible ways, without impacting human reading. They either have to hire more humans to filter the information, or hire more humans to improve the crawlers.
Or we can simply stop sharing knowledge. I'm fine with it, TBF.
Davidzheng
I for one welcome advancement of science and mathematics from our AI overlords
tosapple
And nuclear first strike capabilities.
tgma
Why the AI hate? How is it different from sharing your knowledge with another individual or writing a book to share it?
> AI companies are not paying anyone for that piece of information
So? For the vast majority of human existence, paying for content was not a thing, just like paying for air isn't. The copyright model you are used to may just be too forced. Many countries have no moral qualms about "pirating" Windows and other pieces of software or games (they won't afford to purchase anyway.) There's no inherent morality or entitlement for author receiving payment for everything they "create" (to wit, Bill Gates had to write a letter to Homebrew Computer Club to make a case for this, showing that it was hardly the default and natural viewpoint.) It's just a legal/social contract to achieve specific goals for the society. Frankly the wheels of copyright have been falling off since the dawn of the Internet, not LLM.
program_whiz
Its different because the AI model will then automate the use of that knowledge, which for most people in this forum is how they make their livelihood. If OpenAI were making robots to replace plumbers, I wouldn't be surprised when plumbers said "we should really stop giving free advice and training to these robots." Its in the worker's best interest to avoid getting undercut by an automated system that can only be built with the worker's free labor. And its in the interest of the company to take as much free labor output (e.g. knowledge) as possible to automate a process so they can profit.
tgma
> plumbers
I have received free advice that reduced future need from such actual plumbers (and mechanics and others for that matter)
> we should really stop giving free advice and training to these robots
People routinely freely give advice and teach students, friends, potential competitors, actual competitors, etc on this same forum. Robots? Many also advocate for immigration and outsourcing, presumably because they make the calculus that it is net beneficial in some scenarios. People on this forum contribute to an entire ecosystem of free software, on top of which two kids can and have built $100 billion companies that utilize all such technology freely and without cost. Let's ban it all?
Sure, I totally get if you want to make an individual choice for yourself to keep a secret sauce, not share your code, put stuff behind paywall. That is not the tone and the message here. There is some deep animosity advocating for everyone shutting down their pipes to AI as if some malevolent thing, similar to how Ted Kaczynski saw technology at large.
bgwalter
Companies valued at $300 billion or more are not another individual and people are not "sharing" their works. The companies are stealing them.
For the majority of interesting output people have paid for art, music, software, journalism. But you know that already and are justifying the industry that pays your bills.
tgma
> valued at $300 billion
Irrelevant really. Invoking this in the argument shows the basis is jealousy. They are clearly valued as such not because they collected all the data and stored in some database. Your local library is not worth 300 billion.
> For the majority of interesting output people have paid for art, music, software, journalism
Absolutely and demonstrably false. Music and art predate Copyright by hundreds if not thousands of years.
> But you know that already and are justifying the industry that pays your bills.
Huh, ad hominem much? I find it rich that the whole premise of your argument was some "art, music, software, journalist" was entitled to some payment, but suddenly it is a problem when "my industry" (somehow you assume I work in AI) is getting paid?
artninja1988
Copying something isn't stealing, though.
birktj
Absolutely, I am sceptical of AI omin many ways, but primarily it is about the AI companies and my lack of trust in them. I find it unfortunate that all of the clearly brilliant engineers working at these companies are to preoccupied with always chasing newer and better model trying to reach the dream of AGI do not stop and ask themselves: who are they working for? What happens if they eventually manage to create a model that can replace most or even all of human computer work?
Why whould anyone think that these companies will contribute to the good of humanity when they are even bigger and more powerful, when they seem to care so little now?
LunaSea
> For the vast majority of human existence, paying for content was not a thing
Books were bought, teachers were paid so no, for most of human history information was not free.
observationist
These vigorously held and loudly proclaimed opinions don't matter.
Don't waste the mental energy. They're more interested in performative ignorance and argument than anything productive. It's somewhere between trying to engage Luddites during the industrial revolution and having a reasonable discussion with /pol/ .
They'd rather cling to what they know than embrace change, or get in rhetorical zingers, and nothing will change that except a collision with reality.
GoatInGrey
Counterpoint: in my consulting role, I've directly seen well over a billion dollars in failed AI deployments in enterprise environments. They're good at solving narrow problems, but fall apart in problem spaces exceeding roughly thirty concurrent decision points. Just today I got involved in a client's data migration where the agent (Claude) processed test data instead of the intended data identified in the prompt. It went so far as to rename the test files to match the actual source data files and proceed from there, signalling the all clear as it did. It wasn't caught until that customer, in a workshop said, and I quote "This isn't our fucking data".
ferguess_k
I agree with you. People like me are revisionists. Corporations and States are already rushing to build the most advanced AI, and advancement can be measured in months. We crossed the Rubicon many years ago.
jaggs
I think it's becoming clear that these mega AI corps are juggling with their models at inference time to produce unrealistically good results. By that it seems that they're just cranking up the compute beyond reasonable levels in order to gain PR points against each other.
The fact is most ordinary mortals never get access to a fraction of that kind of power, which explains the commonly reported issues with AI models failing to complete even rudimentary tasks. It's now turned into a whole marketing circus (maybe to justify these ludicrous billion-dollar valuations?).
andy12_
Models drop in price x10 each year. Us, common folk, getting access to these kinds of models is just a matter of time.
jaggs
Is that true though? Having to pay some $200 a month for a max account of whatever kind doesn't seem to be cheaper to me at all?
scarmig
$200/month for an LLM with the capability to fully automate my job is extremely cheap. Of course, even with a high thinking budget we don't have that yet, but if we see it at any cost in 2026, I'll be expecting to be forced into retirement by 2030.
andy12_
When I say 10 times cheaper, I mean when comparing models of the same capabilities. The kind of performance you get now for a 200$ subscription, a year ago probably would have costed 2000$.
Davidzheng
Ok but if they can pump those compute and get science/math advancements it's worth something even if the costs are very high
zeroonetwothree
ICPC problems are about as far from scientific advancements as a spelling bee is from Shakespeare.
(I’m a former ICPC competitor)
Workaccount2
The bleeding edge behind closed doors token burning monsters of 2023 are bad compared to the free LLMs we have now.
I believe it was Sundar in an interview with Lex who said that the reason they haven't developed another Ultra model is because by the time it is ready to launch, the flash and pro versions will have already made it redundant.
jaggs
But then why does every new model release work great for a few weeks, then suddenly performance plummets? It's mysterious?
twhyn2
"It's now turned into a whole marketing circus (maybe to justify these ludicrous billion-dollar valuations?)."
Yes theres an entire ecosystem being built up around language models that has to stay afloat for another 5 years at least, to hope for a significant breakthrough.
Davidzheng
I think part of it depends on whether you see AI progress as research or product.
jaggs
Very good point.
smokel
The best thing of the ICPC is the first C, which stands for "collegiate". It means that you get to solve a set of problems with three persons, but with only one computer.
This means that you have to be smart about who is going to spend time coding, thinking, or debugging. The time pressure is intense, and it really is a team sport.
It's also extra fun if one of the team members prefers a Dvorak keyboard layout and vi, and the others do not.
I wonder how three different AI vendors would cooperate. It would probably lift reinforcement learning to the next level.
NoahZuniga
Actually collegiate means that the contestants are in college.
smokel
Ha, shows what I know :) As a non-native speaker I had always assumed it referred to working with colleagues. Etymologically related, but apparently not the same.
AndrewKemendo
Clearly strongly held Hallucinations are problematic no matter what agent produces them
Workaccount2
Claude, ChatGPT, and Gemini on a team.
I'm not sure how it would play out, but at least when you let them talk to each other they tend to get very technical very fast.
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patrickhogan1
This is impressive.
Here is the published 2025 ICPC World Finals problemset. The "Time limit: X seconds" printed on each ICPC World Finals problem is the maximum runtime your program is allowed. If any judged run of your program takes longer than that, the submission fails, even if other runs finish in time.
https://worldfinals.icpc.global/problems/2025/finals/problem...
HarHarVeryFunny
ICPC = The International Collegiate Programming Contest. These are college level programmers, not elite competitive programmers.
Apparently Gemini solved one problem (running on who knows what kind of cluster) by burning 30 min of "thinking" time on it, and at a cost that Google have declined to provide.
According to one prior competition paricipant, writing in the comments section of this ArsClasica coverage, each year they include one "time sink" problem that smart humans will avoid until they have tackled everything else.
https://arstechnica.com/google/2025/09/google-gemini-earns-g...
This would all seem to put a rather different spin on this. It's not a case of Google outwitting the worlds best programmers, but rather that by searching for solutions for 30 min on god knows what kind of cloud hardware, they were able to get something done that the college kids did not have time to complete, or deem worthwhile starting.
amluto
These are college-student or occasionally grad-school programmers who qualified to enter the ICPC World Finals, generally by performing sufficiently well at a regional championship to qualify. You can read actual rules here (see "Advancing to the ICPC World Finals"):
https://icpc.global/regionals/rules
I don't know what you mean by "elite", and there are certainly plenty of teams at the World Finals that are not especially competitive, and there certainly many elite programers who don't qualify for various reasons (most obviously by being the wrong age or not in the right stage of school or having already attended too many times), but I find it hard to believe that there aren't enough "elite" programmers present to make the winning teams be genuinely elite.
Compare to, say, the Olympics or pretty much any academic olympiad. There are many people and teams at the Olympics who are not remotely competitive with the winners.
Ylpertnodi
> There are many people and teams at the Olympics who are not remotely competitive with the winners.
And yet, they are so much closer to the winners than the people that came 11th, 12th etc.
512
The ICPC has plenty of elite competitive programmers. It's an activity that "peaks" in importance around college, and not many keep training a lot after participating.
Every year there are multiple "Legendary Grandmasters" in the competition. That's >3000 Elo in Codeforces. I'd estimate it takes a similar level of skill/effort as becoming a Chess Grandmaster.
And even those that aren't at that level are very competent at it. The average ICPC participant is likely "smarter" than the average MIT/Harvard CS student for some reasonable measure of "smarter".
HarHarVeryFunny
I didn't realize - didn't mean to disparage anyone.
mannycalavera42
I've competed in these contest before. There are probably more difficult than what we can call _elite_ competitive programmer
note: my team only passed the first 2 rounds, far from bragging about my skills here :)
gerash
ICPC world finals questions are not easy. Idk what your talking about
dist-epoch
Let's bookmark this comment and check again next year, if the freely available models will be able to do it for a few dollars.
HarHarVeryFunny
Sure, although my point wasn't intended to be about the cost (which would still be interesting to know), but rather that the win by Google seems more down to brute force than intelligence.
JohnKemeny
> ICPC = The International Collegiate Programming Contest. These are college level programmers, not elite competitive programmers.
ICPC finalists are very much in the world elite of competitive programmers.
NitpickLawyer
So this year SotA models have gotten gold at IMO, IoI, ICPC and beat 9/10 humans in that atcoder thing that tested optimisation problems. Yet the most reposted headlines and rethoric is "wall this", "stangation that", "model regression", "winter", "bubble", doom etc.
tech_ken
In 2015 SotA models blew past all expectations for engine performance in Go, but that didn't translate into LLM-based Code agents for another ~7 years (and even now the performance of these is up for debate). I think what this shows is that humans are extremely bad at understanding what problems are "hard" for computers; or rather we don't understand how to group tasks by difficulty in a generalizable way (success in a previously "hard" domain doesn't necessarily translate to performance in other domains of seemingly comparable difficult). It's incredibly impressive how these models perform in these contests, and certainly demonstrates that these tools have high potential in *specific areas* , but I think we might also need to accept that these are not necessarily good benchmarks for these tools' efficacy in less structured problem spaces.
Copying from a comment I made a few weeks ago:
> I dunno I can see an argument that something like IMO word problems are categorically a different language space than a corpus of historiography. For one, even when expressed in English language math is still highly, highly structured. Definitions of terms are totally unambiguous, logical tautologies can be expressed using only a few tokens, etc. etc. It's incredibly impressive that these rich structures can be learned by such a flexible model class, but it definitely seems closer (to me) to excelling at chess or other structured game, versus something as ambiguous as synthesis of historical narratives.
edit: oh small world! the cited comment was actually a response to you in that other thread :D
NitpickLawyer
> edit: oh small world the cited comment was actually a response to you in that other thread :D
That's hilarious, we must have the same interests since we keep cross posting :D
The thing with the go comparison is that alphago was meant to solve go and nothing else. It couldn't do chess with the same weights.
The current SotA LLMs are "unreasonably good" at a LOT of tasks, while being trained with a very "simple" objective: NTP. That's the key difference here. We have these "stochastic parrots" + RL + compute that basically solve top tier competitions in math, coding, and who knows what else... I think it's insanely good for what it is.
tech_ken
> I think it's insanely good for what it is.
Oh totally! I think that the progress made in NLP, as well as the surprising collision of NLP with seemingly unrelated spaces (like ICPC word problems) is nothing sort of revolutionary. Nevertheless I also see stuff like this: https://dynomight.substack.com/p/chess
To me this suggests that this out-of-domain performance is more like an unexpected boon, rather than a guarantee of future performance. The "and who knows what else..." is kind of I'm getting: so far we are turning out to be bad at predicting where these tools are going to excel or fall short. To me this is sort of where the "wall" stuff comes from; despite all the incredible successes in these structured problem domains, nobody (in my personal opinion) has really unlocked the "killer app" yet. My belief is that by accepting their limitations we might better position ourselves to laser-target LLMs at the kind of things they rule at, rather than trying to make them "everything tools".
tempusalaria
A lot of the current code and science capabilities do not come from NTP training.
Indeed in seems in most language model RL there is not even process supervision, so a long way from NTP
jug
Even Sam Altman himself thinks we’re in a bubble, and he ought to have a good sense of the wind direction here.
I think the contradiction here can be reconciled by how these tests don’t tend to run on the typical hardware constraints they need to be able do this at scale. And herein lies a large part of the problem as far as I can tell; in late 2024, OpenAI realized they had to rethink GPT-5 since their first attempt became too costly to run. This delayed the model and when it finally released, it was not a revolutionary update but evolutionary at best compared to o3. Benchmarks published by OpenAI themselves indicated a 10% gain over o3 for God knows how much cash and well over a year of work. We certainly didn’t have those problems in 2023 or even 2024.
DeepSeek has had to delay R2, and Mistral has had to delay Mistral 3 Large, teased within weeks back in May. No word from either about what’s going on. DS is said to move more to Huawei and this is behind a delay but I don’t think it’s entirely clear it has nothing to do with performance issues.
It would be more strange to _not_ have people speculate about stagnation or bubbles given these events and public statements.
Personally, I’m not sure if stagnation is the right word. We’re seeing a lot,of innovation in toolsets and platforms surrounding LLM’s like Codex, Claude Code, etc. I think we’ll see more in this regard and that this will provide more value than the core improvements to the LLM’s themselves in 2026.
And as for the bubble, I think we are in one but mostly because the market has been so incredibly hot. I see a bubble not because AI will fall apart but because there are too many products and services right now in a golden rush era. Companies will fail but not because AI suddenly starts failing us but due to saturation.
rhetocj23
Sam Altman proclaiming we are in a bubble benefits him. It lowers the price of potential targets for acquisitions. I bet you didnt think of that did you?
kadushka
it was not a revolutionary update but evolutionary at best compared to o3
It is a revolutionary update if compared to the previous major release (GPT-4 from March 2023).
JohnKemeny
There is a clear difference between what OpenAI manages to do with GPT-5 and what I manage to do with GPT-5. The other day I asked for code to generate a linear regression and it gave back a figure of some points and a line through it.
If GPT-5, as claimed, is able to solve all problems in ICPC, please give the instructions on how I can reproduce it.
theptip
I believe this is going to be an increasingly important factor.
Call it the “shoelace fallacy”: Alice is supposedly much smarter but Bob can tie his shoelaces just as well.
The choice of eval, prompt scaffolding, etc. all dramatically impact the intelligence that these models exhibit. If you need a PhD to coax PhD performance from these systems, you can see why the non-expert reaction is “LLMs are dumb” / progress has stalled.
paxys
Yeah, until OpenAI says "we pasted the questions from ICPC into chatgpt.com and it scored 12/12" the average user isn't really going to be able to reproduce their results.
anthonypasq
the average person doesnt need to do that. The benchmark for "is this response accurate and personable enough" on any basic chat app has been saturated for at least a year at this point.
SamPatt
The average user will never need to answer ICPC questions though.
simianwords
Are you using the thinking model or the non thinking model? Maybe you can share your chat.
JohnKemeny
I prefer not to due to privacy concerns. Perhaps you can try yourself?
I will say that after checking, I see that the model is set to "Auto", and as mentioned, used almost 8 minutes. The prompt I used was:
Solve the following problem from a competitive programming contest. Output only the exact code needed to get it to pass on the submission server.
I need to tackle a problem where no web-based help is available. The task involves checking if a given tree can be the result of inserting numbers 1 to n into an empty skew heap, following the described insertion algorithm. I have to figure out the minimal and maximal permutations that produce such a tree.
minimaxir
The point of the GPT-5 model is that it is supposed to route between thinking/nonthinking smartly. Leveraging prompt hacks such as instructing it to "think carefully" to force routing to the thinking model go against OpenAI's claims.
levocardia
If you can't get a modern LLM to generate a simple linear regression I think what you have is a problem between the keyboard and the chair...
paxys
My response simply is that performance in coding competitions such as ICPC is a very different skillset than what is required in a regular software engineering job. GPT-5 still cannot make sense of my company's legacy codebase even if asked to do the most basic tasks that a new grad out of college can figure out in a day or two. I recently asked it to fix a broken test (I had messed with it by changing one single assertion) and it declared "success" by deleting the entire test suite.
raspasov
This. Dealing with the problems of a real-world legacy code base is the exact opposite of a perfectly constrained problem, verified for internal consistency probably by computers and humans, of all things, and presented neatly in a single PDF. There are dozens, if not 100s, of assumptions that humans are going to make while solving a problem (i.e., make sure you don't crash the website on your first day at work!) that an LLM is not going to. Similar to why, despite all its hype, Waymo cars are still being supervised by human drivers nearly 100% of the time and can't even park themselves regularly without stalling with no explanation.
levocardia
>Waymo cars are still being supervised by human drivers nearly 100% of the time
That seems...highly implausible?
mrkeen
Similar experience with windsurf.
I had a class of 5 or so test methods - ABCDE. I asked it to fix C, so it started typing out B token-by-token underneath C, such that my source file was now ABCBDE.
I don't think I'm smart enough to get it to do coding activities.
77pt77
> it declared "success" by deleting the entire test suite.
The paperclip trivial solution!
riku_iki
> So this year SotA models have gotten gold at IMO, IoI, ICPC > Yet the most reposted headlines and rethoric is "wall this", "stangation that", "model regression", "winter", "bubble", doom etc.
this is narrow niche with high amount of training data (they all buy training data from leetcode), and this results are not necessary generalizable on overall industrial tasks
atleastoptimal
People pattern match with a very low-resolution view of the world (web3/crypt/nfts were a bubble because there was hype, so there must be a bubble since AI is hyped! I am very smart) and fail to reckon with the very real ways in which AI is fundamentally different.
Also I think people do understand just how big of a deal AI is but don't want to accept it or at least publicly admit it because they are scared for a number of reasons, least of all being human irrelevance.
Ianjit
Historically there has been a gap between the performance of AI in test environments vs the impact in the real world, and that makes people who have been through the cycle a few times cautious extrapolating.
In 2016 Geoffrey Hinton said vision models would put radiologists out of business within 5-10 years. 10 years on there is a shortage of Radiologists in the US and AI hasn't disrupted the industry.
The DARPA grand challenge for autonomous vehicles was won in 2006, 20 years on self driving cars still have limited deployment.
The real world is more complex than computer scientists apprecate.
noosphr
Two days ago I talked to someone in water management about data centers. One of the big players wanted to build a center that consumed as much water as a medium town in semi arid bushland. A week before that it was a substation which would take a decade to source the transformers for. Before that it was buying closed down coal power plants.
I don't know if we're in a bubble for model capabilities, but we are definitely hitting the wall in terms of what the rest of the physical economy can provide.
You can't undo 50 years of deffered maintenance in three months.
trhway
Getting well funded commercial demand is exactly how you undo it.
noosphr
Not in three months. It will take years if not decades.
What happens when OpenAI and friends go bust because China is drowning in spare grid capacity and releasing sota open weights models like R1 every other week?
Every company building infrastructure for AI also goes out of business and we are in a worse position than we are now because instead of having a tiny industry building infrastructure at a level required to replace what has reached end of life we have nothing.
Imnimo
My understanding is that the way they do this is have some number of model instances generating solution proposals, and then another model which chooses which candidates to submit.
I haven't been able to find information on how many proposals were generated before a solution was chosen to submit. I'm curious to know whether this is "you can get ICPC gold medal performance with a handful of GPT-5 instances" or "you will drown yourself in API credit debt if you try this".
Still extremely impressive either way.
fullparens
I briefly looked at a few of Gemini's solutions https://github.com/google-deepmind/gemini_icpc2025. What struck me was how Gemini finds clean ways to express an idea - perhaps because it knows a large set of tricks for each kind of sub-algorithm (within the larger algorithm). I am a former competitor and managed to reach world finals at Google CodeJam and Topcoder Open. It took me a lot of work to get there but I will gladly concede that Gemini is way better than my peak. I haven't competed in 15 years and have forgotten a lot of tricks but Gemini's code reminded me how quickly algorithms can get complicated sometimes without a bag of tricks.
There are parallels to tactics in chess - humans might miss them but a machine will not. And that can be a huge difference in a game or even in a software project.
EDIT: minor correction.
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https://x.com/HengTze/status/1968359525339246825