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.
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.
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.
> 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.
> 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.)
OR-Tools is a whole grab-bag of tools, most of which are wrappers around various solvers, including Gurobi and CPLEX. It seems like CP-SAT is under the OR-Tools umbrella, and CP-SAT may well be state-of-the-art for the specific sets of problems that it's well-suited for.
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.
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.
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.
If you want to play that game, let's compute how much energy was spent to grow, house and educate one team since they were born, over 20 years against how much was spent training the model.
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").
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).
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).
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.
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.
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
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.)
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.
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.
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"):
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.
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.
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.
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.
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
> 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.
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".
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.
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.
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.
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.
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.
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.
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.
It did a lot of thinking, including
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.
And I can see that it visited 13 webpages, including icpc, codeforces, geeksforgeeks, github, tehrantimes, arxiv, facebook, stackoverflow, etc.
A terse prompt and expecting a one-shot answer is really not how you'd get an LLM to solve complex problems.
I don't know what Deepmind and OpenAI did in this case, but to get an idea of the kind of scaffolding and prompting strategy that one might want, have a look at this paper where some floks used the normal generally available Gemini Pro 2.5 to solve 5/6 of the 2025 IMO problems: https://arxiv.org/pdf/2507.15855
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.
Just select GPT5-thinking if you need anything done with competence. The regular gpt5 is nothing impressive and geared more towards regular daily life chatting.
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.
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.
It's important to look closely at the details of how these models actually do these things.
If you look at the details of how Google got gold at IMO, you'll see that AlphaGeometry only relies on LLMs for a very specific part of the whole system, and the LLM wasn't the core problem solving system in play.
Most of AlphaGeometry is standard algorithms at play solving geometry problems using known constraints. When the algorithmic system gets stuck, it reaches out to LLMs that were fine tuned specifically for creating new geometric constraints. So the LLM would create new geometric constraints and pass that back to the algorithmic parts to get it unstuck, and repeat.
Without more details, it's not clear if this win is also the Gpt-5 and Gemini models we use, or specially fine-tuned models that are integrated with other non-LLM and non-ML based systems to solve these.
Not being solved purely by LLM isn't a knock on it, but with the current conversations going on today with LLMs, these are heavily being marketed as "LLMs did this all by themselves", which doesn't match with a lot of the evidence I've personally seen.
>This achievement is a significant advance over last year’s breakthrough result. At IMO 2024, AlphaGeometry and AlphaProof required experts to first translate problems from natural language into domain-specific languages, such as Lean, and vice-versa for the proofs. It also took two to three days of computation. This year, our advanced Gemini model operated end-to-end in natural language, producing rigorous mathematical proofs directly from the official problem descriptions – all within the 4.5-hour competition time limit.
AlphaGeometry/AlphaProof (the one you're thinking of, where they used LLMs + lean) was last year! and they "only" got silver. IMO gold results this year were e2e NLP.
People are having a tough time coping with what the near future holds for them. It is quite hard for a typical person to imagine how disruptive and exponential coming world events are like Covid showed.
The last time I asked for a code review from AI was last week. It added (hallucinated) some extra lines to the code and then marked them as buggy. Yes, it beats humans at coding — great!
> 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
I personally view all this stuff as noise. Im more interested in seeing any contributions to the real economy. Not some competition stuff that is irrelevant to the welfare of people.
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".
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.
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.
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.
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.
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.
Which ones in particular? Is your belief all that are companies are inherently malevolent? If not why don't you start one that is not? What's stopping you?
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.
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?
Interference with copyright does not easily equate with theft, conversion, or fraud. The infringer trespasses into the copyright owner’s domain, but he does not assume physical control over the copyright nor wholly deprive its owner of its use. Although it is no less unlawful or wrongful for that reason, it is not a theft.
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?
"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?"
Have you seen the people who do OpenAI demos? It becomes pretty apparent upon inspection, what is driving said people.
Not sure by what metric you compare the difficulty, but regardless of the hardness of the problem, IIRC, ICPC requires 100% correctness on test cases to score a problem (even failing one means you don't get the score,) but IOI would admit fractional scores (correct me if I am wrong.)
For fractional scores, it depends on problems. In short, there are two types of problems in IOI. One is traditional problems that requires 100% correctness, and the other is continuous scoring.
The prior can still results in score between 0 and 100, but this is because there are subtasks in the problem. For example, a graph become a tree or even just a linear sequence. Nonetheless, you still need to ensure your algorithm is correct on all testcases in that subtask in order to get the score of that task.
Because I'm a ICPC medalist (not this year though) but not a IOI medalist.
Another evidence is that you only have 5 hours to solve 3 problems in IOI, but you need to solve 10+ problems in ICPC. It's impossible to have all 10+ problems to at IOI level in ICPC.
OK. I think my opinion and definition on "easier" is indeed vague. For "easier", I'm only comparing the thinking difficulty.
Yes, medal is function of ranking but not difficulty.
Nonetheless, I would say that IOI more focus on thinking, which I to some degree is not that good at, while ICPC is more like a mix thinking and implementing. Therefore, my ability to implement stuff can improve my ICPC ranking but not IOI.
As a former ICPC winner, I'd say ICPC is mainly a test of teamwork, given the format of the competition (3 team mates, one computer, scoring that rewards clean solutions submitted quicker, tackled in the optimal order for your three sets of skills, etc).
Sure, you need to be individually good at thinking, etc. But the difference between 1st and places further down the ranking is teamwork.
As a former ICPC participant, albeit not first place (hats off to you), I would generally characterize it as "having a good team," much more so than what's usually considered "teamwork." It is a parallelization/scheduling effort than deep interpersonal collaboration.
(In a certain sense, this is actually the ideal "teamwork" setup in the industry as well, to have a bunch of people who own their part and are trusted by their colleagues take care of it and not step on each other toes than kumbaya let's all get together on the same problem.)
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?).
"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.
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.
$200/month for an LLM with the capability to fully automate my job is extremely cheap. Of course, even with a high thinking budget we don't have that yet, but if we see it at any cost in 2026, I'll be expecting to be forced into retirement by 2030.
When I say 10 times cheaper, I mean when comparing models of the same capabilities. The kind of performance you get now for a 200$ subscription, a year ago probably would have costed 2000$.
You don't? Now I use Gemini to code and optimize CUDA kernels. When I first used GPT3 in the OpenAI playground I was extremely impressed when I managed to get it to output a hello world program in C.
I understand what you're saying. However I'm not sure it's that germane when we're talking about whether or not the current $200 subscription fee is actually delivering value for money, or whether AI giants are manipulating performance to gain marketing points.
A database is good at leetcode, who would have thought. Give humans a database and they'll outperform your "AI" (which probably uses an extraordinary amount of graphics cards and electricity).
It is an idiotic benchmark, in line with the rest of the "AI" propaganda.
Whats the point? These models are still unreliable in every day work. And they're getting fat! For a moment, they were getting cheaper, but now they are only getting bigger and this is not going to be cheap in the future. The point is, what are we investing a trillion dollars in?
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.
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.
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.
I don’t think you’ll find many race tracks that permit horses and cars to compete together.
(I did enjoy the sarcasm, though!)
> 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.
> 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.)
> 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/
OR-Tools is a whole grab-bag of tools, most of which are wrappers around various solvers, including Gurobi and CPLEX. It seems like CP-SAT is under the OR-Tools umbrella, and CP-SAT may well be state-of-the-art for the specific sets of problems that it's well-suited for.
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.
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.
Human teams are limited to three people. So why doesn’t it matter how many instances they used?
Human brains and cloud instances are not remotely equivalent. What you can compare on an equivalent basis is cost.
All instances of any given model are kinda the same, for lack of a better word, "person": same knowledge, same skills, same failings.
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.
If you want to play that game, let's compute how much energy was spent to grow, house and educate one team since they were born, over 20 years against how much was spent training the model.
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").
Ha so true. I was so tempted to copy and paste a problem into GPT5 and see what it would say
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).
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
Sounds like a bug. Did you try it again (or with another leading-edge model) and get a similar result?
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.
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.
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
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.)
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.
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.
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.
> 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.
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 :)
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.
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.
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.
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.
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.
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
> 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.
> 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".
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
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.
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.
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).
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.
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.
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.
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.
Are you using the thinking model or the non thinking model? Maybe you can share your chat.
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:
It did a lot of thinking, including And I can see that it visited 13 webpages, including icpc, codeforces, geeksforgeeks, github, tehrantimes, arxiv, facebook, stackoverflow, etc.A terse prompt and expecting a one-shot answer is really not how you'd get an LLM to solve complex problems.
I don't know what Deepmind and OpenAI did in this case, but to get an idea of the kind of scaffolding and prompting strategy that one might want, have a look at this paper where some floks used the normal generally available Gemini Pro 2.5 to solve 5/6 of the 2025 IMO problems: https://arxiv.org/pdf/2507.15855
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.
Just select GPT5-thinking if you need anything done with competence. The regular gpt5 is nothing impressive and geared more towards regular daily life chatting.
Are you sure? I thought you can only specify reasoning_effort and that's it.
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.
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.
It's important to look closely at the details of how these models actually do these things.
If you look at the details of how Google got gold at IMO, you'll see that AlphaGeometry only relies on LLMs for a very specific part of the whole system, and the LLM wasn't the core problem solving system in play.
Most of AlphaGeometry is standard algorithms at play solving geometry problems using known constraints. When the algorithmic system gets stuck, it reaches out to LLMs that were fine tuned specifically for creating new geometric constraints. So the LLM would create new geometric constraints and pass that back to the algorithmic parts to get it unstuck, and repeat.
Without more details, it's not clear if this win is also the Gpt-5 and Gemini models we use, or specially fine-tuned models that are integrated with other non-LLM and non-ML based systems to solve these.
Not being solved purely by LLM isn't a knock on it, but with the current conversations going on today with LLMs, these are heavily being marketed as "LLMs did this all by themselves", which doesn't match with a lot of the evidence I've personally seen.
>This achievement is a significant advance over last year’s breakthrough result. At IMO 2024, AlphaGeometry and AlphaProof required experts to first translate problems from natural language into domain-specific languages, such as Lean, and vice-versa for the proofs. It also took two to three days of computation. This year, our advanced Gemini model operated end-to-end in natural language, producing rigorous mathematical proofs directly from the official problem descriptions – all within the 4.5-hour competition time limit.
[1]https://deepmind.google/discover/blog/advanced-version-of-ge...
AlphaGeometry/AlphaProof (the one you're thinking of, where they used LLMs + lean) was last year! and they "only" got silver. IMO gold results this year were e2e NLP.
Well, the supposed PhD-level models are still pretty dumb when they get to consumers, so what gives?
People are having a tough time coping with what the near future holds for them. It is quite hard for a typical person to imagine how disruptive and exponential coming world events are like Covid showed.
The last time I asked for a code review from AI was last week. It added (hallucinated) some extra lines to the code and then marked them as buggy. Yes, it beats humans at coding — great!
What's "It?" What was your prompt?
> 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
Don't worry, they're just stochastic parrots copying answers from Stack Overflow. ;)
This comment makes me think. What did previous winners of these competition go on to do in their lives? Anything spectacular?
Indeed.
I personally view all this stuff as noise. Im more interested in seeing any contributions to the real economy. Not some competition stuff that is irrelevant to the welfare of people.
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.
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.
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.
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.
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.
> 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.
the AI isn't malevolent (... yet)
but the companies operating it certainly are
they have no concept of consent
they take anything and everything, regardless of copyright or license, with no compensation to the authors
and then use it to directly compete with those they ripped off
not to mention shoving their poor quality generated slop everywhere they can possibly manage, regardless of ethics, consent or potential consequences
children should not be supplied a sycophantic source of partial truths that has been instructed to pretend to be their friend
this is text book malevolence
> but the companies operating it certainly are
Which ones in particular? Is your belief all that are companies are inherently malevolent? If not why don't you start one that is not? What's stopping you?
I don't think I need to give a list
> What's stopping you?
from doing what?
I don't want shitty AI slop; why would I start a company intent on generating it?
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.
> 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?
Copyright is not the same as paying for it
Copying something isn't stealing, though.
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?
"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?"
Have you seen the people who do OpenAI demos? It becomes pretty apparent upon inspection, what is driving said people.
I for one welcome advancement of science and mathematics from our AI overlords
Given that ICPC problems are in general easier than IOI problems. I wouldn't be surprise to see they can get Gold (even perfect scores) in ICPC.
Nonetheless, I'm still questioning what's the cost and how long it would take for us to be able to access these models.
Still great work, but it's less useful if the cost is actually higher than hiring someone with the same level.
Not sure by what metric you compare the difficulty, but regardless of the hardness of the problem, IIRC, ICPC requires 100% correctness on test cases to score a problem (even failing one means you don't get the score,) but IOI would admit fractional scores (correct me if I am wrong.)
I compare the difficulty by solving them myself.
For fractional scores, it depends on problems. In short, there are two types of problems in IOI. One is traditional problems that requires 100% correctness, and the other is continuous scoring.
The prior can still results in score between 0 and 100, but this is because there are subtasks in the problem. For example, a graph become a tree or even just a linear sequence. Nonetheless, you still need to ensure your algorithm is correct on all testcases in that subtask in order to get the score of that task.
What makes you say that they are easier? Are there more people who manages to solve a problem from ICPC than from IOI?
How do you compare those?
There were at least 2 very simple problems in IOI this year.
I haven't read the ICPC problem set, and perhaps there are some low-hanging fruits, but I highly doubt it.
Because I'm a ICPC medalist (not this year though) but not a IOI medalist.
Another evidence is that you only have 5 hours to solve 3 problems in IOI, but you need to solve 10+ problems in ICPC. It's impossible to have all 10+ problems to at IOI level in ICPC.
> Because I'm a ICPC medalist (not this year though) but not a IOI medalist.
Isn't getting a medal a function of your ranking, not score, in both cases? If so, that does not prove much about the difficulty of either.
OK. I think my opinion and definition on "easier" is indeed vague. For "easier", I'm only comparing the thinking difficulty.
Yes, medal is function of ranking but not difficulty.
Nonetheless, I would say that IOI more focus on thinking, which I to some degree is not that good at, while ICPC is more like a mix thinking and implementing. Therefore, my ability to implement stuff can improve my ICPC ranking but not IOI.
As a former ICPC winner, I'd say ICPC is mainly a test of teamwork, given the format of the competition (3 team mates, one computer, scoring that rewards clean solutions submitted quicker, tackled in the optimal order for your three sets of skills, etc).
Sure, you need to be individually good at thinking, etc. But the difference between 1st and places further down the ranking is teamwork.
As a former ICPC participant, albeit not first place (hats off to you), I would generally characterize it as "having a good team," much more so than what's usually considered "teamwork." It is a parallelization/scheduling effort than deep interpersonal collaboration.
(In a certain sense, this is actually the ideal "teamwork" setup in the industry as well, to have a bunch of people who own their part and are trusted by their colleagues take care of it and not step on each other toes than kumbaya let's all get together on the same problem.)
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?).
"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.
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.
But then why does every new model release work great for a few weeks, then suddenly performance plummets? It's mysterious?
Models drop in price x10 each year. Us, common folk, getting access to these kinds of models is just a matter of time.
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?
$200/month for an LLM with the capability to fully automate my job is extremely cheap. Of course, even with a high thinking budget we don't have that yet, but if we see it at any cost in 2026, I'll be expecting to be forced into retirement by 2030.
When I say 10 times cheaper, I mean when comparing models of the same capabilities. The kind of performance you get now for a 200$ subscription, a year ago probably would have costed 2000$.
I don’t believe that current models are 1000x better than the initial ChatGPT release. What metric are you using?
You don't? Now I use Gemini to code and optimize CUDA kernels. When I first used GPT3 in the OpenAI playground I was extremely impressed when I managed to get it to output a hello world program in C.
I understand what you're saying. However I'm not sure it's that germane when we're talking about whether or not the current $200 subscription fee is actually delivering value for money, or whether AI giants are manipulating performance to gain marketing points.
I assume the original reply was addressing the “never” in this specific point:
“The fact is most ordinary mortals never get access to a fraction of that kind of power”
Ok but if they can pump those compute and get science/math advancements it's worth something even if the costs are very high
ICPC problems are about as far from scientific advancements as a spelling bee is from Shakespeare.
(I’m a former ICPC competitor)
Two words: Uh oh
Sharing links to a couple of tweets is not a blog post.
Google source post: https://deepmind.google/discover/blog/gemini-achieves-gold-l... (https://news.ycombinator.com/item?id=45278480)
OpenAI tweet: https://x.com/OpenAI/status/1968368133024231902 (https://news.ycombinator.com/item?id=45279514)
Make that shit cure cancer/disease and abstain from that modern Space race equivalent BS ffs.
A database is good at leetcode, who would have thought. Give humans a database and they'll outperform your "AI" (which probably uses an extraordinary amount of graphics cards and electricity).
It is an idiotic benchmark, in line with the rest of the "AI" propaganda.
Where is this magic ICPC competition answers database that they're using?
Whats the point? These models are still unreliable in every day work. And they're getting fat! For a moment, they were getting cheaper, but now they are only getting bigger and this is not going to be cheap in the future. The point is, what are we investing a trillion dollars in?
/> The point is, what are THEY investing a trillion dollars in?
Who cares? I won't be a customer until I see a return on my investment [in them].